Evaluation of potential casei strains

by

Kanokwan Tandee

A dissertation submitted in partial fulfillment of

the requirements for the degree of

Doctor of Philosophy

(Food Science)

at the

UNIVERSITY OF WISCONSIN-MADISON

2013

Date of final oral examination: 12/21/12

The dissertation is approved by the following members of the Final Oral Committee:

James L. Steele, Professor, Food Science

Kirk L. Parkin, Professor, Food Science

Amy C. Wong, Professor, Bacteriology

Thomas D. Crenshaw, Professor, Animal Science

Benjamin J. Darien, Associated Professor, Medical Science

i

Table of contents

Abstract ii

Acknowledgements iv

List of figures v

List of tables vii

Chapter 1 Introduction 1

Chapter 2 Literature review 6

Chapter 3 Evaluation of potential probiotic Lactobacillus casei strains in 41

an in vitro gastrointestinal model and piglets

Chapter 4 Transit of Lactobacillus casei 32G through the piglet ileum and 96

its effect on the composition of the ileum microbiota

Chapter 5 Dose-dependent impact of Lactobacillus casei 32G on 141

the mouse cecum microbiota

Chapter 6 Summary and recommendation for future studies 164

ii

Evaluation of potential probiotic Lactobacillus casei strains

Kanokwan Tandee

Under the supervision of Professor James Steele

At the University of Wisconsin-Madison

Probiotics are live microorganisms which, when administered in adequate amounts, confer a health benefit on the host. Lactobacillus casei, due to its high level of consumption, is a probiotic of particular interest. Significant genetic variability exists within this species with approximately 38% of the genes being variable between L. casei strains. This variability suggests that significant strain-to-strain variation in probiotic efficacy and effects is likely. This thesis describes methods for screening L. casei strains for attributes commonly associated with through the establishment of in vitro and in vivo models, and utilizes culture-independent methods to characterize the influence of consumption of L. casei on the composition of the gastrointestinal tract (GIT) microbiota of piglets and mice. Strain- specific differences were observed in the ability to survive gastric passage and adhere to the piglet ileum epithelial surface. These results led to L. casei 32G being selected for further characterization. The ability of L. casei 32G to alter the ileum digesta and epithelial tissue adherent microbiotas was examined in two separate piglet feeding trials. In both studies, significant changes were detected in the dominant genera in both the digesta and tissue samples; however, the specific genera that increased and decreased differed between the two iii trials. The second piglet trial also examined these alterations in the piglet ileum microbiota over time after the last dose. The results indicated that daily consumption of 32G resulted in significant, relatively short-lived alterations (hours) to the composition of both the digesta and Peyer’s patch microbiotas. The influence of 32G dose on the ability of this strain to alter the composition of the GIT microbiota was evaluated in mice. The results demonstrated that dose-dependent changes occur in the cecum microbiota of mice upon administration of L. casei 32G and that the lowest dose examined (106 CFU/day for seven days) had the most dramatic impact on this microbiota. In conclusion, L. casei 32G was selected as the strain examined with the greatest probiotic potential and was shown to cause restructuring of the

GIT microbiotas in both the piglet and mouse models. iv

Acknowledgements

I am highly grateful for Dr. James Steele for his guidance and advice throughout my graduate study.

I would like to thank my graduate committees - Dr. Kirk Parkin, Dr. Amy Wong, Dr.

Thomas Crenshaw, and Dr. Benjamin Darien, as well as Dr. Jeff Broadbent and Dr. Nasia

Safdar for their suggestions.

I appreciate the technical assistance from Steele’s lab members - Dr. Vladimir

Smeianov, Mateo Budinich, Dr. Hui Cai, Willyn Tan, Busra Aktas, Pamella Wipperfurth,

Kari Nevermann, Dr. Ekkarat Phrommao, Kurt Selle, Lulu Hoza, Samuel Garber, Emily

Engelhart, Jeehwan Oh, Elena Vinay-Lara, Jessie Heidenreich, Neil Gandhi, Davide

Porcellato, Chokchai Chuea-nongthon, Pingfan Wu, Michael Donath, and Travis De Wolfe as well as students from University of Wisconsin-Madison Department of Animal Science, students from University of Wisconsin-River Falls Department of Animal and Food Science,

Dr. Viriya Nitteranon, Simone Warrack, and Megan Duster.

I finally thank Thai Government Ministry of Science and Technology for the scholarship as well as my family and friends for their encouragement and support. v

List of figures

Chapter 3

Figure 1 Survival of L. casei strains in the in vitro GI model 74

Figure 2 Detection of L. casei strains on the distal ileum-derived tissue 75

Figure 3 Discrimination between ileum digesta microbiotas of control and 76

32G fed piglets

Figure 4 Discrimination between ileum tissue microbiotas of control and 77

32G fed piglets

Figure S1 Detection of L. casei strains in the piglet intestinal digesta 83

Figure S2 Relative quantity of 16S-23S rRNA spacer amplicons found in the 84

control or 32G fed piglet digesta

Figure S3 Discrimination between piglet ileum digesta and tissue microbiotas 85

Chapter 4

Figure 1 Transit of L. casei 32G in the piglet ileum digesta and Peyer’s patch 120

Figure 2 Clustering of the piglet ileum digesta microbiota at the level 121

by condition and sampling time after the last dose

Figure 3 Clustering of the piglet ileum Peyer’s patch microbiota at the genus 122

level by condition and sampling time after the last dose

Figure 4 Composition of piglet ileum digesta microbiota at the phylum or 123

genus level

Figure 5 Composition of piglet ileum Peyer’s patch microbiota at the phylum 124

or genus level vi

Figure S1 Discrimination between piglet ileum digesta and Peyer’s patch 130

microbiotas

Chapter 5

Figure 1 Detection of Lactobacillus, coliform , and class Clostridia 160

in the mouse cecum

Figure 2 Composition of mouse cecum microbiota characterized by ARISA 161 vii

List of tables

Chapter 3

Table 1 Origins and references for L. casei strains 78

Table 2 Log reductions of L. casei strains in the in vitro GI model and piglet 79

Table 3 Bacterial phyla, classes, orders, and families that differ between 80

control and 32G-fed piglets

Table 4 Bacterial genera that differ between control and 32G fed piglets 82

Table S1 Humanized diet for 10 kg piglets per day 86

Table S2 Nutrient composition of the piglet humanized diet per day 87

Table S3 Log reductions of four L. casei strains in each compartment of 89

the in vitro GI model

Table S4 Normalized weights by intestinal section of piglet digesta 90

Table S5 pH and content of digesta from the last section of 91

small intestines of control or 32G fed piglets

Table S6 ARISA profile of each piglet 92

Table S7 Bacterial genera detected in control or 32G fed piglets 93

Chapter 4

Table 1 Changes in composition of piglet ileum digesta microbiota resulting 125

from administration of L. casei 32G

Table 2 Changes in composition of piglet ileum Peyer’s patch microbiota 127

resulting from administration of L. casei 32G.

Table S1 Bacterial genera that differ between control and 32G-fed piglets 131 viii

at 1.5 h after the last dose

Table S2 Bacterial genera that differ between control and 32G-fed piglets 133

at 6 h after the last dose

Table S3 Bacterial genera that differ between control and 32G-fed piglets 135

at 12 h after the last dose

Table S4 Bacterial genera that differ between control and 32G-fed piglets 137

at 24 h after the last dose

Table S5 Bacterial genera that differ between control and 32G-fed piglets 139

at 72 h after the last dose

Chapter 5

Table 1 Average numbers of Lactobacillus, coliform bacteria, and Clostridia 162

in the mouse cecum

Table 2 Identification of bacterial colonies isolated from Brucella agar plates 163

1

CHAPTER 1

Introduction

2

Our gastrointestinal tract (GIT) hosts over 1014 cells from at least 395 species of commensal microorganisms, which are 10 times the number of our own cells (5). The enormous number and complexity are an indication of the importance of our GIT microbiota.

Additionally, the GIT microbiota is considered a human organ, as the changes in levels and compositions can influence metabolism and physiology involved in human health and disease (7). These microorganisms, mostly bacteria, have evolved in this complex and dynamic habitat and continued adapting as niches changed through a lifetime. Therefore, it is necessary to understand the role of the GIT microbiota to ensure the homeostasis that ultimately promotes our well-being.

The GIT microbiota is destabilized by exogenous modifications, e.g. diet change, pathogenic infection, or antibiotic use, as well as endogenous factors, e.g. aging, obesity, or stress. Destabilization of the GIT microbiota can lead to imbalances in this microbiota, referred to as dysbiosis, which may ultimately result in disease (4). The ability of commensal bacteria to recover to a typical state is individual-specific. This is primarily due to genetic background, with factors such as metabolic rate and immune function thought to have key roles. Additionally, diet and exercise also play crucial role in controlling the GIT microbiota. Not surprisingly, health-promoting diets have drawn the public attention because of their impact on the digestive system and an indirect effect on the GIT microbiota.

Furthermore, the concept of probiotics, which means “for life” in Greek, has emerged and gained popularity during the past century. This concept was first proposed by Elie

Metchnikoff based upon his belief that consumption of beneficial bacteria in foods, such as , modified the intestinal flora and prevented illness, thereby enhancing longevity in

Bulgarian peasants (1). 3

The Food and Agriculture Organization (FAO) and the World Health Organization

(WHO), have defined probiotics as live microorganisms which when administered in adequate amounts confer a health benefit on the host (3). Clearly, beneficial effects are the ultimate goal of probiotic consumption. While several research groups have concentrated on the indirect effects, such as modulation of immune responses or prevention of systemic infections, the most apparent impacts of probiotic administration are related to the composition of GIT microbiota. Competition within the diverse ecological niches for adhesion site between probiotics and commensa bacteria are thought to be the primary reason for this effect. In addition, probiotics and the commensal microbiota interact with the host immune system, which also has a key role in structuring the commensal microbiota (6, 9).

The effectiveness of probiotics in reconstructing the commensal microbiota is, therefore, dependent on the levels and types of commensal bacteria present in GIT, as well as the physiological and immunological integrity of the host (8).

In this dissertation, Lactobacillus casei has been chosen as a probiotic of interest mainly due to the high level of consumption globally in various commercial products such as

Yakult (L. casei Shirota), Actimel (L. casei DN-114001) or DanActive (DN-114001). In addition, five complete (ATCC 334, BL23, Zhang, BD-II, and LC2W) and 12 draft (12A,

21/1, 32G, A2-362, CRF28, Lc-10, Lpc-37, M36, T71499, UCD174, UW1, and UW4) L. casei genomes are available. Comparative analysis has demonstrated that a high level of genetic variability exists within this species (2). Therefore, there is a high probability that other L. casei strains exist with preferable probiotic attributes; for example, the ability to change the GIT microbiota toward a healthier composition. These strains could be 4 administered to humans as probiotics for a specific purpose such as prevention and treatment of antibiotic-associated diarrhea, thereby improving human health.

The ultimate goal of our research on probiotics is to develop a L. casei strain(s) with defined health benefits and to determine its mechanism(s) of action. In this dissertation, three studies were conducted to screen L. casei strains for potential probiotic attributes and to characterize the influence of the selected strain on the commensal GIT microbiotas.

The first study, which is presented in Chapter 3, reports the screening of L. casei strains in our culture collection for the ability to survive the GIT passage, adhere to ileum epithelium, and influence the intestinal microbiota using in vitro and piglet models. The second study, which is presented in Chapter 4, examined the transit of L. casei 32G, the strain of interest from the first study, through the piglet ileum. Additionally, its impact on the ileum digesta and Peyer’s patch microbiotas was followed over a 24 h period. The third study, which is presented in Chapter 5, examined the dose-dependent effect of L. casei 32G on the cecum microbiota in a mouse model.

Further research is required to understand the mechanism(s) responsible for the observed changes in the commensal microbiota resulting from administration of L. casei 32G and to determine if health benefits are related to the observed changes. 5

References

1. Anukam, K. C., and G. Reid. 2008. Probiotics: 100 years (1907-2007) after Elie Metchnikoff's observations, p. 466-474. In A. Mendez-vilas (ed.), Communicating Current Research and Educational Topics and Trends in Applied Microbiology, 2007 ed. Formatex.org, Spain.

2. Broadbent, J. R., E. C. Neeno-Eckwall, B. Stahl, K. Tandee, H. Cai, W. Morovic, P. Horvath, J. Heidenreich, N. T. Perna, R. Barrangou, and J. L. Steele. 2012. Analysis of the Lactobacillus casei supragenome and its influence in species evolution and lifestyle adaptation. BMC Genomics 13:533.

3. FAO/WHO. 2001. Health and nutritional properties of probiotics in food including powder with live . Report of a joint FAO/WHO expert consultation.

4. Gareau, M. G., P. M. Sherman, and W. A. Walker. 2010. Probiotics and the in intestinal health and disease. Nat Rev Gastroenterol Hepatol 7:503-514.

5. Ley, R. E., D. A. Peterson, and J. I. Gordon. 2006. Ecological and evolutionary forces shaping microbial diversity in the human intestine. Cell 124:837-848.

6. Sanders, M. E. 2011. Impact of probiotics on colonizing microbiota of the gut. J Clin Gastroenterol 45 Suppl:S115-S119.

7. Sekirov, I., S. L. Russell, L. C. M. Antunes, and B. B. Finlay. 2010. Gut microbiota in health and disease. Physiological Reviews 90:859-904.

8. Veiga, P., C. A. Gallini, C. Beal, M. Michaud, M. L. Delaney, A. DuBois, A. Khlebnikov, J. E. van Hylckama Vlieg, S. Punit, J. N. Glickman, A. Onderdonk, L. H. Glimcher, and W. S. Garrett. 2010. animalis subsp. lactis fermented milk product reduces inflammation by altering a niche for colitogenic microbes. Proc Natl Acad Sci U S A 107:18132-18137.

9. Wells, J. M., O. Rossi, M. Meijerink, and P. van Baarlen. 2011. Epithelial crosstalk at the microbiota-mucosal interface. Proc Natl Acad Sci U S A 108 Suppl 1:4607-4614.

6

CHAPTER 2

Literature review

7

1. Probiotics

FAO and WHO have defined the probiotics as live microorganisms which when administered in adequate amounts confer a health benefit on the host (17); however, inclusion of the word "live" remains somewhat controversial. This is due to the fact that dead cells and parts of cells have been shown to have positive effects, primarily immunomodulatory activity. It is important to note that health benefits, e.g. reduced incidence of colds and flus, are microbial strain and dose specific. While there is strong evidence supporting that probiotics have health benefits, their mechanisms of action remain undefined. There has been a large body of research reported utilizing in vitro systems to examine probiotic mechanisms of action; however, these studies frequently do not predict in vivo efficacy. Therefore, there is a need to develop robust in vivo models to further our understanding of probiotic mechanisms of action.

1.1. Health benefits

1.1.1. Local effects

Health benefits of probiotics can be found locally (in gastrointestinal tract; GIT) or systemically (throughout the body). GIT diseases that have been shown to respond to probiotics are infectious diarrhea, gastric ulcers caused by , irritable bowel syndrome, antibiotic-associated diarrhea, Clostridium difficile-associated disease, inflammatory bowel disease, traveler’s diarrhea, necrotizing enterocolitis, constipation, lactose intolerance, and colorectal cancer (8). 8

Diarrhea is defined as three or more loose or watery stools within 24 hours; it is considered persistent when the illness lasts longer than 14 days (2). While rotavirus is the most prevalent infectious agent, other agents such as adenovirus, enterovirus, enterotoxigenic

E. coli, , , Yersinia, Campylobacter, Vibrio cholera, Cryptosporidium, and Giardia are also common (2). Meta-analysis showed that probiotics reduced the acute diarrhea on the third day; the duration of diarrhea was also shortened by 30.48 hours (2). For example, L. reuteri DSM 17938, which was derived from antibiotic-resistant strain ATCC

55730, could improve the acute diarrhea in hospitalized children by decreasing the duration and frequency of watery diarrhea (18). In addition to diarrhea resulting from pathogens, antibiotic treatment can cause diarrhea by disturbing the microbial balance, with the symptoms ranging from mild and self-limiting to severe. Probiotics such as L. rhamnosus and Saccharomyces boulardii can restore the GIT microbiota and prevent antibiotic- associated diarrhea in children (31). A systematic review and meta-analysis of randomized controlled trials, which evaluated the efficacy of Lactobacillus, Bifidobacterium,

Saccharomyces, Streptococcus, , and Bacillus for the prevention or treatment of this disease, indicated a correlation between probiotic administration and reduced risk of antibiotic-associated diarrhea (27). However, effectiveness between populations, antibiotic treatment, and probiotic composition cannot be determined due to variation between studies

(27). S. boulardii was effective against traveler’s diarrhea, -related diarrhea, and H. pylori-related symptoms as revealed by meta-analysis; promising outcomes were also observed in C. difficile-associated disease, irritable bowel syndrome, acute adult diarrhea,

Crohn’s disease, giardiasis, and HIV-related diarrhea with S. boulardii (44). 9

Ulcerative colitis and Crohn’s disease are considered as inflammatory bowel diseases

(IBD) caused by dysregulated host-bacteria interactions. This uncontrolled intestinal inflammation can later develop into colorectal cancer (21). While ulcerative colitis is limited to the distal colon and rectum, Crohn’s disease can be found in any part of GIT, but it is primarily found in the distal ileum and cecum (35). The first inflammation is more superficial and driven by Th2 lymphocytes; the latter can be granulomatous inflammation and involved in Th1 responses (21). In addition to the different composition of mucosa- associated microbiota, some IBD patients also have genetic background that is involved in the bacterial recognition and clearance such as M cell, macrophage, neutrophil, dendritic cell, defensin-producing Paneth cell, and adaptive immune responses (35). Although no specific pathogen has been determined as a causative agent, probiotics could be effective to prevent or treat IBD by balancing the GIT microbiota and modulating the immune responses (35).

Remission in ulcerative colitis was significantly increased in children after administration

VSL#3, which consists of L. paracasei, L. plantarum, L. acidophilus, L. delbrueckii subsp. bulgaricus, B. longum, B. brevis, B. infantis, and Streptococcus thermophilus (47). In severe cases, ulcerative colitis is treated by removing the colon and forming an ileal pouch. A common difficulty in patients with an ileal pouch is inflammation, referred to as pouchitis.

VSL#3 has been shown to maintain remission in pouchitis (48) and increase the number and diversity of mucosa-associated microbiota toward anaerobes (36). Probiotics have promise for the treatment of IBD; however, insufficient clinical evidence exists to warrant regulatory approval. 10

Irritable bowel syndrome (IBS) is characterized by sporadic abdominal pain, altered bowel movement, and other symptoms such as bloating and without any structural abnormalities (45). The causes can be multifactorial as abnormal gut motility, visceral hypersensitivity, disturbed neural function of the brain-gut axis and an abnormal autonomic nervous system (39). Therapeutic treatment of IBS is not available and the current strategy only alleviates specific symptoms (10). Therefore, an effective treatment might be restoration of the microbiota composition, which is an additional factor affecting IBS pathogenesis (39). Meta-analysis suggested that probiotics can be used to improve global

IBS symptoms. Promising strains included E. faecalis, B. faecalis, a mixture of L. rhamnosus GG, L. rhamnosus LC705, B. brevis Bb99, and Propionibacterium freudenreichii, and a mixture of E. coli and E. faecalis (45). L. casei subsp. rhamnosus LCR3 has been demonstrated to be effective against the IBS diarrhea (10) and L. plantarum 299v (DSM

9843) has shown to relieve abdominal pain and bloating in IBS patients after a four-week treatment (15). Also, a mixture of L. rhamnosus GG, L. rhamnosus LC705, B. brevis Bb99, and P. freudenreichii was shown to decrease Ruminococcus torques and stabilize Clostridium thermosuccinogenes in the feces from IBS patients (41). Finally, L. brevis KB290 alleviated the frequencies of watery or mushy feces and abdominal pain in children by increasing

Bifidobacterium and lowering Clostridium (51). Probiotics show promise for treating IBS, but additional clinical studies are required to identify optimal strains and doses.

Probiotics have been shown to decrease pathogenic translocation after injury; for example, L. plantarum L2 completely prevented the bacterial infection in mesenteric lymph node, liver, spleen, and kidney and reduced the secretion of pro-inflammatory cytokines and 11 endoepithelial cell apoptosis (78). As the result, the disrupted cecal microbiota and the ileal mucosa integrity were recovered.

1.1.2. Systemic effects

Probiotics can be used to reduce the incidence of cold and flu, ear-nose-throat infection, urogenital infection, atopic dermatitis, allergic rhinitis, or used as a vaccine adjuvant (40). As probiotics are orally administered, these effects suggest the role of immune or blood components.

Clinical trials have indicated that infections in the upper respiratory tract, such as rhinitis, rhinosinusitis, rhinopharyngitis or common cold, pharyngitis, epiglottitis and laryngitis, could be relieved after probiotic administration. This approach is considered as an alternative to antibiotic treatment that can cause resistance in pathogens (58). While rhinoviruses, coronaviruses, parainfluenza and influenza are the common cause of respiratory tract infection, bacterial pathogens such as group A streptococci, Mycoplasma pneumoniae,

Chlamidia pneumoniae, Corynebacterium diphteriae, Staphylococcus aureus, and

Streptococcus pneumonia are also causative agents (58). The best documented probiotic species that have been shown effective in this application are L. rhamnosus and L. acidophilus. Other species such as L. delbrueckii, L. paracasei, L. plantarum, L. casei, L. helveticus, B. animalis, B. longum, and B. bifidum have also been shown to be effective in other studies (58).

The use of probiotics in critically ill patients remains controversial; however, their low cost and ease of administration are considered as advantages over antibiotic treatment for 12 ventilator-associated pneumonia in an intensive care unit. Administration of probiotics, such as L. rhamnosus and L. plantarum 299, or synbiotic containing Pediococcus pentosaceus,

Leuconostoc mesenteroides, L. paracasei subsp. paracasei, and L. plantarum resulted in the lower incidence of this upper respiratory tract infection (69). Moreover, these strains decreased the length of intensive care unit stay and Pseudomonas aeroginosa colonization, although there was no change in mortality rate, duration of mechanical ventilation, and diarrhea (69).

Uropathogenic E. coli is the most frequent cause of urinary tract infection, which is primarily developed by the bacterial translocation from the bladder or kidney. Other causative agents include Klebsiella, Proteus, and Enterobacter. The infection activates cytokine secretion and neutrolphil attraction into urinary tract to clear out the pathogens. If antibiotics are used as a primary treatment, the complications such as kidney infection and septicemia are common, suggesting the application of microbiota to control the pathogenic expansion. A mouse model of urinary tract infection has shown that oral administration of L. plantarum LPLM-O1 as a prophylactic therapy could decrease E. coli counts and leukocyte numbers, without any adverse effects (11).

Incomplete immune development during childhood is considered one of the causes of atopic dermatitis in human. Although L. paracasei Lpc-37, L. acidophilus 74-2, and B. animalis subsp. lactis DGCC 420 did not improve the skin conditions in patients with atopic dermatitis, the peripheral immune parameters were modulated and symptoms seemed to be alleviated (64). The study also observed the different responses in healthy adults and patients, suggesting the role of host immune status and GIT microbiota in probiotic efficacy 13

(64). It is thought that the development of allergies related to the respiratory tract, such as allergic rhinitis, sinusitis, and asthma, may be due to the low exposure to microorganisms during childhood (70). Singh and Das reviewed the application of probiotics to modify the

GIT immune system and systemically prevent or treat the respiratory allergies. Safe and effective treatment was observed in allergic rhinitis, but not in controlling mild and moderate asthma (70).

Several studies suggested the potential of probiotics to serve as vaccine adjuvants by boosting the serum antibodies and improving immunization, underscoring the influence of probiotics on systemic immune responses. Paineau and colleagues observed an increase of serum IgG in healthy adults receiving B. lactis Bl-04 or L. acidophilus La-14 after oral cholera vaccination (54). Although there was no significant change in the overall vaccine effectiveness, B. lactis Bi-07 and L. acidophilus NCFM were shown to affect the secretion of different antibodies (54). A study by Rizzardini and colleagues also supported an adjuvant effect of probiotic strains on the seasonal influenza vaccination. After administration to healthy adults, B. animalis subsp. lactis BB-12 and L. paracasei subsp. paracasei 431 were able to elevate a concentration of vaccine-specific IgG, IgG1, and IgG3 in serum and secretory IgA in saliva (62). These changes are considered as an adaptive immune response from both Th1 (IgG3) and Th2 (IgG1) lymphocytes and were specific to influenza vaccine since tetanus-specific IgG was not affected; however, the strains did not influence the incidence of influenza infection and innate immune responses such as cytokines, phagocytosis and natural killer cell activity (62). Another study reported the induction of neutralizing antibodies and poliovirus-specific IgA and IgG in serum of healthy adults after 14 administered L. rhamnosus GG or L. acidophilus CRL431 with the concentration of antibodies not being different between these probiotics (12). Poliovirus naturally infects via an oral route, propagates in intestinal mucosa or lymphatic tissue, and spreads to the central nervous system, leading to the paralysis. Thus, increase in circulating antibodies would likely neutralize the virus and prevent the disease (12).

1.2. Mechanisms of action

Probiotic and host can interact directly (e.g. host-microbe interaction) or via intermediates (e.g. microbe-microbe interaction) to elicit the health benefits described. In general, probiotics are thought to result in the beneficial effects via producing antimicrobials, competing with pathogens for nutrients and adhesion sites, inducing host defensin secretion, modulating immune responses, enhancing intestinal barrier function, or altering the gut microbiota (53). Determination of the mechanisms of action of probiotics is necessary to guide further research on probiotics and to select probiotic strains for specific purposes such as prophylactic and therapeutic therapies of infectious diseases.

1.2.1. Antimicrobial production

Short-chain fatty acids (e.g. acetic acid) and lactic acid are major metabolites from several probiotics genera including Lactobacillus and Bifidobacterium. When produced in the GIT, these molecules decrease the luminal pH and inhibit acid-sensitive bacteria. For example, anti-infectious activity against E. coli O157:H7 of B. brevis Yakult and B. pseudocatenulatum DSM 20439 was shown to result from the production of acetic acid and 15 low pH in the intestine (4). These strains directly inhibited Shiga toxin production via a combination of acetic acid and acidic pH in vitro, thereby reducing weight loss and mortality in infected mice (4). In addition to short-chain fatty acids and lactic acid, other antimicrobials produced include hydrogen peroxide, nitric oxide, and bacteriocins.

Bacteriocins are ribosomally synthesized antimicrobial peptides produced by bacteria and archaea (13). They are diverse, varying in size, structure, mode of action, antimicrobial potency, immunity mechanisms and target cell receptors. Bacteriocins may impact GIT in several ways: introducing a producer into an established niche (colonizing peptides); inhibiting other strains or pathogens (killing peptides); and signaling other bacteria through quorum sensing, or modulating the host immune system (signaling peptides), which eventually affect the GIT microbiota (13). However, their in vivo production and function are influenced by strain survival, specific activity, dosing regimen, animal model, and target organisms (13).

1.2.2. Competitive exclusion

Probiotics are able to compete with pathogenic microorgansims for adhesion sites and nutrients, leading to the inhibition of pathogenesis. For example, E. coli Nissle 1917 has been shown to interfere with the regulation of virulence factors in Salmonella typhimurium, such as adhesin (SiiE) and transcription factor (HilA) that controls invasiveness, via the production of secretory molecules (68). Although the extracellular and intracellular growth of Salmonella was not affected, Nissle 1917 mutant lacking fimbria and flagella was less inhibitory toward Salmonella; these results indicate that the secretory molecules were not 16 bactericidal and that competitive adhesion was necessary for preventing Salmonella infection

(68). Adhesion and invasion of adherent-invasive E. coli to intestinal epithelial cells were inhibited after L. casei DN-114001 was co-incubated with E. coli or pre-incubated with intestinal cells (29). Supernatant of DN-114001 also promoted this inhibitory effect and increased its adhesion to intestinal cells, which further reduced the adhesion site for E. coli.

DN-114001 might produce other antimicrobials to reduce the E. coli growth since pH- neutralized supernatant elicited the same result (29).

1.2.3. Immunomodulation

Immunomodulation, which results from interactions between microorganisms and epithelial cells or mucosal immune cells, is another probiotic attribute that can impact their clinical efficacy. Probiotics (and other bacteria) consist of microbe-associated molecular patterns (MAMP) that bind to host cells via pattern recognition receptors (PRR) such as Toll- like receptors (TLR), nucleotide oligomerization domain (NOD)-like receptors and C-type lectin receptors (80). These MAMPs include metabolites, cell surface proteins and such as lipoteichoic acid and peptidoglycan, non-methylated cytosine-guanine dinucleotide (CpG), which further activate the expression of surface receptors, secreted cytokines and chemokines (80). For example, different strains of bacteria can variably stimulate TLR2 due to differences in the expression and secretion levels of certain lipoproteins, the amount and structure of lipoteichoic acid, and the effect of shielding factors such as exopolysacchrides. Moreover, carbohydrates used in the post-translational modification of surface proteins can bind to C-type lectin binding receptors. The host 17 signaling system can result in increased barrier function and defensin production, as well as regulate inflammation via the modulation of T cell subsets and humoral immunity (80). E. coli Nissle 1917, L. fermentum PZ1162 and Pediococcus pentosaceus LMG P-20608 were shown to induce the production of human β-defensin-2, which acts as a cationic antimicrobial peptide in the innate immune response and as a chemokine in intestinal epithelial cells, via transcription factors NF-κB and AP-1 (79). As the result, the epithelial barrier against infection was strengthened (79).

A neonatal gnotobiotic pig model for human rotavirus, which was developed to avoid the influence of other bacteria and viruses, has been used to demonstrate that L. acidophilus and L. reuteri affected the Th1 and Th2 cytokine responses to human rotavirus infection by increasing a level of IL-12, IFN-γ, IL-4, and IL-10 in serum (5). Additionally, Th1 cytokine- secreting cells in other lymphoid tissues were systemically activated. These probiotic strains also maintained gut homeostasis by controlling the TGF-β production (5). Several surface proteins of probiotics were determined to be involved in immunomodulation. For example,

SpaCBA polymeric pili, which consist of SpaC mucus-binding adhesin at the tip, of L. rhamnosus GG were shown to control the expression of IL-8 in intestinal epithelial cells

(37). Removal of these pili not only reduced the adhesion capacity and biofilm formation, but also increased the level of IL-8, probably due to interactions between other surface components (such as lipoteichoic acid) and TLR2 (37). Another sensing molecule from probiotics is genomic DNA, as L. rhamnosus GG and B. longum exhibited an anti- inflammatory property by attenuating the transcription factor NF-κB activity and IL-8 level 18 in intestinal epithelial cells via TLR9 signaling (23). Probiotic DNA also has been shown to promote formation of tight junctions and integrity of the epithelial cell monolayer.

1.2.4. Change in microbiota composition

Introducing probiotics into GIT can affect the activities of commensal bacteria. B. longum NCC2705 and L. casei DN-114001 have been shown to expand the utilization of Bacteroides thetaiotaomicron ATCC 29148, a dominant saccharolytic symbiont in human GIT, towards dietary plant polysaccharides such as mannoside and xyloside (71).

Co-colonization of NCC2705 or DN-114001 with Bacteroides thetaiotaomicron ATCC

29148 in germ-free mice suggested that the impact of probiotic strains on GIT microbiota in human may occur via direct (e.g. production of sensing molecules) or indirect mechanisms

(e.g. change in substrate availability) (71). Another example was found in humanized gnotobiotic mice colonized with human baby microbiota, where L. paracasei was shown to change the amino acid profiles by using the different for protein catabolism. As the result, there was a decrease of Staphylococcus, which might become less compatible to the new niche (43). The effect of L. rhamnosus DR20 on human fecal microbiota was investigated during a 15-month period (74). Results suggested that the strain, which became undetectable after six months of consumption, transiently changed the composition of the commensal Lactobacillus population (74). The proposed mechanism was that L. rhamnosus

DR20 elicited an immune response that altered the commensal Lactobacillus population.

L. salivarius UCC118 has been shown to significantly decrease the proportion of phylum Spirochates (e.g. Treponema) in pigs and phylum Bacteriodetes (e.g. Bacteroides, 19

Tannerella) in mice; however, phylum (e.g. Parasporobacterium,

Faecalibacterium, Papillibacter, Ethanoligenens) were promoted in mice (61). Even though the overall microbial diversity was not altered by UCC118, the reduction of Spirochates and

Bacteriodetes was considered advantageous, since these organisms can cause opportunistic infections (61). The production of bacteriocin Abp118 was partially involved in this anti- infective effect since a bacteriocin-defective mutant exerted lower activity against these bacteria (61). L. johnsonii La1 producing a non-bacteriocin antimicrobial has been shown to reduce lecithinase-positive Clostridium in healthy adults (81). Since this bacterium could secrete carcinogenic enzymes such as nitroreductase and azoreductase, its inhibition was considered beneficial (81). The administration of the multispecies probiotics VSL#3 was shown to treat inflammatory bowel disease partly via reduction of B. pseudolongum (60).

Since there was only organism, S. thermophilus, recovered from the cecum, the therapeutic effect of VSL#3 was due to the influence on inflammation-related bacteria (60). Similar result was observed in piglets received L. sobrius S1 since Streptococcus suis, which generally caused meningitis, pneumonia, septicemia, and arthritis in pigs, was inhibited (73).

The use of probiotics to reduce the incidence of allergic diseases has been an area of intense research interest for more than a decade. Of the organisms that have been investigated for this application, L. rhamnosus GG is the best characterized. L. rhamnosus

GG has been shown to alter the GIT microbiota of infants at high risk of developing asthma after administration for six months (9). The fed strain was present in feces at a high level; in addition, the complexity of the microbiota was increased with increases in abundance of phylogenetically related bacteria, such as L. crispatus, L. salivarius, L.sakei, L. 20 manihotivorans, L. suntoryeus, L. kitasatonis, L. cypricasei, L. fuchuensis, and B. bifidum

(9). As a result, the GIT was resistant to perturbation and pathogenic outgrowth and there was a lower risk for development of asthma and atopy. The probiotic mechanism, which protects against the allergic diseases, was suggested to be the promotion of multiple types of microorganisms, rather than a single species, to create a stable and balanced microbiota (9).

Similarly, L. paracasei Lpc-37, L. acidophilus 74-2, and B. animalis subsp. lactis DGCC 420 have been shown to reduce Clostridium perfringens cluster I-II in healthy adults, although the effect was not observed in patients with atopic dermatitis (63). Since the concentration of short-chain fatty acids e.g. acetate, propionate, butyrate, valerate, and caproate was not altered, other probiotic/host metabolites might be responsible for the Clostridium decrease

(63). This finding contradicts a previous study reporting that B. animalis subsp. lactis DN-

173010 created a non-permissive environment for Klebsiella pneumoniae and Proteus mirabilis, which were colitogenic bacteria in mice, by decreasing cecal pH and changing short-chain fatty acid profiles (76). DN-173010 also resulted in an increase in lactate- consuming and butyrate-producing bacteria such as Desulfovibrio, Anaerostipes caccae, and

Eubacterium hallii (76). Other microorganisms have also shown to alter the composition of the GIT microbiota. For example, L. delbrueckii subsp. bulgaricus and S. thermophilus, which are the organisms present in yogurt starter cultures, have been shown to significantly increase C. perfringens in healthy adults after the yogurt consumption (20), while the

Bacteroides-Porphyromonas-Prevotella group, including Bacteroides vulgatus, was reduced

(20). Similarly, L. plantarum 299v when administered for two weeks to patients undergoing a colonoscopy for polyps has been shown to promote the level of anaerobic bacteria in rectal 21 biopsies (24). Changes in GIT microbiota can be correlated with the disease outcomes, such as was observed when a multispecies probiotic (B. bifidum W23, B. animalis subsp. lactis

W52, L. acidophilus W70, L. casei W56, L. salivarius W24, and Lactococcus lactis W58) was used as a prophylactic treatment of acute pancreatitis. Although the normal microbiota was not restored after disease development, the probiotic mixture increased the level of a commensal ileal bacterium closely related to Clostridium lituseburense (22). As the result, there were decreases in total bacterial overgrowth and translocation, pancreas pathology, and plasma proinflammatory cytokines (22). In addition to live probiotics, a lysate of L. casei

DN-114001 has been shown to change the microbiota composition, by increasing the abundant of Butyricicoccus, Coprococcus, and Anaerostipes (82). It was that these butyrate- producing bacteria were promoted by the elevated level of lactate-producing Lactobacillus.

Since butyrate plays a role in energy homeostasis for colonocytes, the gut barrier function was enhanced and the lysate-treated mice were less susceptible to intestinal inflammation

(82). As the result, the severity of colitis in mice was partly alleviated by the modification of microbial population (82). Possible mechanisms by which the DN-114001 lysate affected these changes are quorum-sensing molecules and/or antimicrobials (e.g. short-chain fatty acids and bacteriocins), inhibition of secretion of pro-inflammatory cytokines, or induction of production of host antimicrobials (e.g. defensins). These studies provide overwhelming evidence that probiotics can change the composition of the GIT microbiota; however, linking these changes to beneficial health outcomes remains an area requiring research attention.

22

1.3. Adverse effects

Although beneficial effects are the most common outcome in clinical trials and

Lactobacillus and Lactococcus have generally recognized as safe (GRAS) status (65), opportunistic infections resulting from probiotic administration have also been reported with immune-compromised or critically ill people. For example, a mixture of L. acidophilus, L. casei, L. salivarius, Lactococcus lactis, B. bifidum, and B. lactis increased enterocyte damage and bacterial translocation in patients with acute pancreatitis, which is developed by bacterial overgrowth in small intestine, mucosal barrier failure, and a pro-inflammatory response leading to bacterial translocation (7). While the rate of complications was comparable to the placebo group, the mortality rate was higher; thus, probiotics are not recommended for patients with acute pancreatitis (6). Similarly, S. boulardii, which is considered a non- pathogenic yeast, accounted for 3.6% of fungemia in France (55). Use of broad spectrum antibiotics possibly played a role in this high incidence, since susceptibility to infection by S. boulardii increased after microbiota resistance collapsed. Sporadic cases of lactobacillemia have also been reported; for example, L. rhamnosus, L. casei, and L. paracasei were isolated from blood of patients after organ transplantation at a rate of 0.241%. This relatively low rate underscores the low virulence of Lactobacillus (67). B. brevis was also found to be a causative agent of neonatal meningitis (52). However, infants in another study showed no side effects but gained more weight and had fewer abnormal abdominal signs after B. brevis was administered (33). Moreover, meta-analysis has shown that probiotics when given to patients in an intensive care unit reduced the incidence of ventilator-associated pneumonia, length of stay, and colonization by Pseudomomas aeroginosa in the respiratory tract (69). 23

Additionally, L. casei Shirota has been determined to be safe for critically ill children (72).

These studies indicate that probiotics have been associated with infectious diseases at a very low rate and that probiotics may be inappropriate in some critically ill patient population.

The potential for transmission of antibiotic-resistance determinant is also a concern with probiotics. For example, plasmid-encoded antibiotic-resistance genes for tetracycline, erythromycin, and chloramphenicol have been identified in L. fermentum, L. acidophilus, L. reuteri, and L. plantarum (65). Additionally, some species of Lactobacillus, Leuconostoc, and Pediococcus have high intrinsic resistance to vancomycin (1), a glycopeptide antibiotic that targets the peptidoglycan of Gram-positive bacteria (65). However, in these organisms the genetic determinant is chromosomally encoded, not inducible, and has not been shown to be transmissible. Screening for transmissible antibiotic-resistance genes is an essential part of the safety evaluation of probiotics.

2. Lactobacillus casei as a probiotic

Bifidobacterium and Lactobacillus are the most common bacterial genera utilized as probiotics (59). Among the species consumed by humans as probiotics, L. casei is consumed at the highest levels based on number of doses and organisms. The beneficial effects related to the consumption of L. casei are primarily reductions in the incidence of infectious diseases in both the respiratory tract and GIT, suggesting that both local and systemic modulation of immune system occurs.

24

2.1. Health benefits

Similar to other probiotics, the effect of L. casei on the host can be categorized into local and systemic benefits. An example of a local benefit is the reduction of C. perfringens cluster I-II in healthy adults after the consumption of a probiotic mixture containing L. paracasei Lpc-37. The authors suggested the observed effect was due to metabolites other than short-chain fatty acids, since the concentration of acetate, propionate, butyrate, valerate, and caproate was not altered (63). A similar result was reported by Pirker and colleagues, when L. casei Shirota was consumed during antibiotic treatment and lower incidence of antibiotic-associated diarrhea and C. difficile infection, as well as higher diversity of fecal microbiota were observed (56). The proposed mechanism was that Clostridium XI was inhibited by antibiotics and that L. casei Shirota enhanced the recovery of the commensal microbiota, thereby preventing pathogenic growth and colonization (56). Additionally, antibiotic administration decreased the abundance of butyrate producer Clostridium cluster

IV and expression of butyryl-CoA CoA transferease gene (56). As butyrate produced by the commensal microbiota has a role in the prevention of diarrhea (i.e. by controlling the uptake of fluid and electrolytes, providing the energy to intestinal epithelial cells, and ensuring the morphological and functional integrity of colonocytes), this change may have also influence antibiotic-associated diarrhea and C. difficile infection rates.

Antibiotic-associated diarrhea (AAD), which is a major cause of diarrhea in hospitalized patients with an incidence rate varies between 10-30%, is resulted from the change in balance and diversity of the GIT microbiota composition (66). The symptoms can start within a few hours after the first antibiotic dose and last for two months after 25 discontinuation. Two separate large placebo-controlled double-blinded randomized clinical trials indicated that consumption of L. acidophilus CL1285 and L. casei LBC80R during the antibiotic treatment was effective in preventing AAD in adult patients (19, 66). Results from these large studies indicate that this probiotic mixture reduced the incidence and duration of diarrhea, as well as minimizing the disruption of intestinal microbiota. In addition, the incidence and duration of C. difficile-associated diarrhea (CDAD) were significantly reduced in a single-center study; efficacy was determined to be dose-dependent with a dose of 100 billion CFU resulting in a lower CDAD incidence rate than a dose of 50 billion CFU (19).

Meta-analysis of randomized, placebo-controlled studies confirmed the prevention of

C. difficile infection by a combination of L. casei LBC80R and L. acidophilus CL1285, even though the intervention was more achievable when the microbiota was not greatly altered

(i.e. the probiotics were less effective in patients with recurrent infection) (30). C. difficile infection, which is responsible for 15-25% of AAD and 90-100% of antibiotic-associated pseudomembranous colitis, is recognized by profuse diarrhea, abdominal cramping, fever, and leukocytosis (77). Approximately 1–3% of healthy adults carry the C. difficile, while the rate is higher in hospital patients and long-term care facility residents (77). Pathogenesis is related to disruption of the normal flora, often by antibiotic treatment, followed by colonization by C. difficile; symptoms can range from asymptomatic to severe diarrhea and colitis due to the toxin production (77). In the randomized double-blinded placebo- controlled study, L. casei DN-114001 and yogurt bacteria (S. thermophilus and L. bulgaricus) were shown to reduce the incidence of AAD and CDAD in hospitalized elderly

(28). 26

As mentioned previously, the stimulation of intestinal immune cells (e.g. macrophage and dendritic cells) can have systemic effects on immune function. Evidence supporting systemic effects was obtained by demonstrating that L. casei DN-114001 in a drinking yogurt reduced the incidence of common infectious diseases in children attending daycare centers/schools in a patient-oriented, cluster-randomized, double-blinded, placebo-controlled, three-month clinical trial (46). Reductions were observed in GIT and upper respiratory tract infections, but not lower respiratory tract infection (46). However, there was no difference in the disease symptoms, such as vomiting, stomach pain, constipation, running nose, cough, decreasing appetite, fever, and rash, when compared to the placebo group (46). L. casei DN-

114001 has also been shown to be effective in decreasing numbers of episodes and cumulative durations of common infectious diseases (i.e. upper respiratory tract infection such as rhinopharyngitis) in the independent elderly in a randomized double-blinded controlled multicenter study (25). While DN-114001 was detectable in stools during the three-month study, no significant difference in cumulative number or severity of infections, intensity or duration of fevers, pathogen detection, medication prescribed, blood immune parameters, or quality of life scores were observed (25).

The ability of L. casei to reduce markers of metabolic syndrome has also been investigated. L. casei Shirota was administered and inflammation parameters such as neutrophil function and TLR expression, as well as levels of lipopolysaccharide-binding protein, soluble CD14, and high-sensitive C-reactive protein followed (38). No improvement in these markers of inflammation was observed; however, the patient recruitment process has 27 been questioned as inflammatory markers in these patients were not different from healthy individuals (38).

2.2. Mechanisms

The ability of L. casei to survive in the small intestine is partly due to its ability to tolerate bile, which is a strain-specific trait. Comparative analysis of whole cell proteomes in six L. casei, including the most bile tolerant strains Rosell-215, F-19 and CRL431, the most sensitive strain ATCC 334, and moderately tolerant strains DN-114001 and Shirota has been conducted (26). Twelve out of 29 proteins identified had previously been reported to be involved in bile tolerance; these determinants were involved in membrane modification

(NagA, NagB, and RmlC), cell protection and detoxification (ClpL and OpuA), as well as central metabolism (Eno, GndA, Pgm, Pta, Pyk, Rp1l, and ThRS) (26). However, none of the potential bile tolerance markers identified had a bile salt hydrolase activity, suggesting bile tolerance is not associated with this activity in L. casei (26).

Similar to other probiotics, surface proteins of L. casei can play a role in adhesion to the intestinal epithelial surface which is thought to be related to persistence in and interaction with a host. A putative fibronectin-binding protein has been characterized on the surface of

L. casei BL23 (50). Additional screening for adhesins resulted in the identification of three proteins i.e. a putative transcriptional regulator, a hypothetical protein, and a putative phage- related endolysin (49). The putative transcriptional regulator has been associated with the expression of genes involved in pentose metabolism. The third protein possesses a signal peptide and cell wall-binding domain that was reported to interact with the extracellular 28 matrix (49). Although a secretion signal peptide was not identified in the second protein, it exhibited strong binding activity to extracellular matrix (ECM) proteins (i.e. collagen, fibronectin, and fibrinogen) (49). These proteins were able to bind to ECM proteins in vitro, but their influence on adhesion to the intestinal epithelium needs to be determined (49).

The molecular mechanisms by which L. casei DN-114001 enhanced the intestinal epithelial barrier, which consists of the apical plasma membrane and intercellular tight junctions, have been characterized (16). The strain prevented epithelial barrier dysfunction in Caco-2 cells by lowering the paracellular permeability, increasing the transepithelial electrical resistance, and promoting in the expression of zonula occludens-1, which is a tight junction protein linked to cytoskeleton (16). The mitogen activated protein kinase (MAPK) signaling pathway was implicated in the preservation of tight junction-associated barrier integrity as L. casei DN-114001 stimulated the expression of TLR2 and p-Akt proteins (16).

While immunomodulation is a common factor determining the probiotic efficacy, L. casei can affect the host and nearby microorganisms in several ways. In the duodenal mucosa of healthy adults, L. casei CRL431 induced growth- and development-promoting transcriptional regulators, such as jun oncogene and a cell cycle regulator NB WEE1 homolog, that are involved in cell proliferation, blood-vessel development, hormonal secretion, immune- response regulation (TLR3, TLR9, IFN regulatory factors, and IFN-regulated genes), and immune tolerance (endothelin 1, insulin receptor substrate 2, and cytokine-inhibitory peptide adrenomedullin) (75). Genes encoding interleukins and lymphocyte surface receptors were also up-regulated, suggesting that this strain affected Th2 immune responses (75). In the treatment of listeriosis in a mouse model using L. paracasei CNCM I-3689 and L. casei 29

BL23, the transcriptomes of mouse and Listeria monocytogenes were simultaneously analyzed. Both probiotic strains not only limited the invasion of L. monocytogenes to the spleen and liver, but also repressed the expression of mouse IFN-stimulated genes that were increased upon infection (3). In contrast, three microRNAs (miR-192, miR-200b, and miR-

215), which were inhibited during infection, were induced by L. casei BL23 (3). Results suggested that these probiotics can modulate the expression of host genes altered by a pathogen. In addition, both strains promoted the production of IL-2 and IL-10, which are the anti-inflammatory cytokines (3). For the pathogenic transcriptome, these probiotics significantly increased the expression of enzymes involved in propanediol and ethanolamine catabolism and cobalamin biosynthesis, forcing L. monocytogenes towards different carbon and nitrogen sources since Lactobacillus cannot utilize ethanolamine (3). These studies highlight the complex interactions that occur between probiotics, the host, and the host GIT microbiota. Unraveling these complex interactions is a significant hurdle to understanding probiotic mechanisms of action.

Immunomodulation has been shown to be essential for the beneficial health outcomes of several L. casei probiotic trials. In a trial with L. casei CRL431 to prevent or reduce

Candida albicans infection in the spleen, liver, blood, and lung, the numbers of macrophage and neutrophil showed to increase in the peritoneal cavity and respiratory tract, suggesting the circulation of activated immune cells from the intestine (42). Additionally, the phagocytic and bactericidal activities of alveolar and peritoneal macrophages were increased when compared to a control infection group (42). IFN-γ and TNF-α, cytokines that can activate the innate immunity, were also raised in serum, peritoneal and broncho-alveolar 30 fluids; ex vivo assays later showed that an increase in these cytokines resulted from the activated macrophages (42). Another example was a study demonstrating that healthy adults consuming L. paracasei subsp. paracasei 431 had elevated levels of IgG, IgG1, and IgG3 in serum and secretory IgA in saliva (62). These changes resulted from both Th1 and Th2 lymphocytes and were specific to the influenza vaccine since tetanus-specific IgG was not affected; however, the strain did not alter the incidence of influenza infection and innate immune responses such as cytokines, phagocytosis and natural killer cell activity (62). The effect of L. casei Shirota on T cell activation was investigated using human peripheral blood mononuclear cells and the higher expression of CD69 on CD4+ and CD8+ T cells and CD25 on CD8+ T cell was observed (14). Production of both proinflammatory and anti- inflammatory cytokines, such as IL-1β, IL-6, TNF-α, IL-12 and IL-10, was observed; however, IL-10 and IL-6 were inhibited in the presence of lipopolysaccharide (14).

Therefore, L. casei Shirota enhanced the cytotoxic T lymphocytes that can destroy infected cells, possibly via the induction of proinflammatory cytokines (14). Another study suggested that L. casei DN-114001 was able to induce the cytokine production, e.g. IL-1β, TNF-α, IL-

6, IL-10, and IFN-γ, in human and mouse immune cells via TLR9, which recognizes unmethylated CpG, but not via TLR2, TLR4, NOD2, C-type lectin receptor dectin 1, mannose receptor, and DC-SIGN (57). The probiotics with low immunogenicity were, however, recommended for inflammatory bowel diseases. The mechanism(s) by which L. casei strains modulate immune function remain to be elucidated.

The ability of a probiotic strain to change or restore the GIT microbiota is a probiotic mechanism of interest, as changes in the GIT microbiota can substantially impact the health. 31

The ability of L. casei DN-114001 to change the intestinal microbiota was reported when its lysate promoted an increase in Butyricicoccus, Coprococcus, and Anaerostipes in the mouse feces (82). There was also an increase in Lactobacillus, which typically produce lactate as their metabolic end product. The lactate produced could support the growth of butyrate- producing bacteria; therefore, changes in microbiota composition observed may be partly due to elevated levels of lactate (82). Since colonocytes can utilize butyrate as an energy source, the gut barrier function was enhanced and the lysate-treated mice were less susceptible to intestinal inflammation; microbiota change in this study, therefore, resulted in the lower severity of colitis in mice (82). L. casei DN-114001 has also been shown to reduce the level of Clostridium in children with atopic dermatitis, when this strain was examined for three months of consumption and for five months after supplementation (34). Although the levels of Bacteroides, Enterococcus, and were not changed, an increase in

Bifidobacterium numbers was observed (34). The reduction in Clostridium and increase in

Bifidobacterium are considered as a possible mechanism of action, as increases in

Clostridium and reductions in Bifidobacterium have been associated with atopic dermatitis.

When L. casei Shirota and B. breve Yakult were introduced to infants with severe congenital anomalies via a nasogastric tube, the dominant intestinal microbiota was changed into commensal bacteria such as Bacteroides, Bifidobacterium, Lactobacillus,

Enterobacteriaceae, and Enterococcus (32). Thus, the colonization resistance was established and enterocolitis was prevented; the nutritional state and body weight were also improved (32). Correlating alterations of the GIT microbiota to the consumption of 32 probiotics can health benefits is an important first step to understanding the complex relationship between GIT microbiota, probiotics, and health. 33

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57. Plantinga, T. S., W. W. van Maren, J. van Bergenhenegouwen, M. Hameetman, S. Nierkens, C. Jacobs, D. J. de Jong, L. A. Joosten, B. van't Land, J. Garssen, G. J. Adema, and M. G. Netea. 2011. Differential Toll-like receptor recognition and induction of cytokine profile by Bifidobacterium breve and Lactobacillus strains of probiotics. Clin Vaccine Immunol 18:621-628.

58. Popova, M., P. Molimard, S. Courau, J. Crociani, C. Dufour, F. Le Vacon, and T. Carton. 2012. Beneficial effects of probiotics in upper respiratory tract infections and their mechanical actions to antagonize pathogens. J Appl Microbiol doi:10.1111/j.1365- 2672.2012.05394.x.

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61. Riboulet-Bisson, E., M. H. Sturme, I. B. Jeffery, M. M. O'Donnell, B. A. Neville, B. M. Forde, M. J. Claesson, H. Harris, G. E. Gardiner, P. G. Casey, P. G. Lawlor, P. W. O'Toole, and R. P. Ross. 2012. Effect of bacteriocin Abp118 on the mouse and pig intestinal microbiota. PLoS One 7:e31113. 39

62. Rizzardini, G., D. Eskesen, P. C. Calder, A. Capetti, L. Jespersen, and M. Clerici. 2012. Evaluation of the immune benefits of two probiotic strains Bifidobacterium animalis ssp. lactis, BB-12(R) and ssp. paracasei, L. casei 431(R) in an influenza vaccination model: a randomised, double-blind, placebo- controlled study. Br J Nutr 107:876-884.

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41

CHAPTER 3

Evaluation of potential probiotic Lactobacillus casei strains in an in vitro

gastrointestinal model and piglets

Kanokwan Tandee,1 Pamella Wipperfurth,2 Mateo Budinich,1 Bonnie Walters,2 Thomas

Crenshaw,3 Jeff Broadbent,4 and James Steele1

Department of Food Science, University of Wisconsin-Madison1; Department of Animal and

Food Science, University of Wisconsin-River Falls2; Department of Animal Science,

University of Wisconsin-Madison3; and Department of Nutrition, Dietetics, and Food

Sciences, Utah State University4

42

Abstract

This study reports screening of L. casei strains DN-114001, 32G, ATCC 334, and

UWP for attributes believed to be important for probiotic efficacy. An in vitro model and piglets were utilized to assess strain-specific survival during gastrointestinal passage to the ileum. The two models yielded different survival rates, but similar rank orders of survival, with L. casei DN-114001 exhibiting the least survival, indicating that the in vitro model has utility in screening potential probiotic strains. Adherence of the four strains to piglet ileum was examined and L. casei 32G was determined to adhere at a significantly higher level than the other three strains examined. L. casei 32G was also determined to be metabolically active in the ileum, as indicated by an increase in the luminal L-lactate level. Between group analysis of 16S rRNA pyrosequencing data indicated that feeding L. casei 32G resulted in a significant change in the ileum digesta microbiota, but not in the tissue adherent microbiota.

Correspondence analysis of these data sets demonstrated that significant changes occurred in the dominant genera in both the digesta and tissue samples. The numerically most significant differences between control and 32G-fed piglet ileum digesta microbiotas were reductions in the preponderance of Turicibacter (30.05 vs. 0.08%) and Clostridium (22.96 vs. 0.08%) and increases in the percentage of Lactobacillus (15.86 vs. 38.28%) and Actinobacillus (< 0.0005 vs. 15.83%). These results suggest that of the strains examined, L. casei 32G had survival, adherence, and antimicrobial characteristics that are desirable in probiotic strains.

43

Introduction

Probiotics are live microorganisms which, when administered in adequate amounts, confer a health benefit on the host (20). A wide array of human health benefits have been attributed to the consumption of probiotics (12); the majority of which can be divided into either gastrointestinal (GI) or immune health benefits. Human GI health benefits ascribed to probiotics include reduced incidence of GI infections, improved lactose digestion, regulation of bowel transit time, and reduction in incidence of colon cancer (15, 26, 28, 48, 53).

Immune health benefits ascribed to probiotics include reduction in the incidence of inflammatory bowel diseases, enhanced immune responses, reduction in allergic reactions, and anti-inflammatory activity (25, 42, 43, 57). The most common genera of probiotics are

Lactobacillus and Bifidobacterium (4). There is significant interest in number of species within these genera; however, Lactobacillus casei, due to its high level of consumption (> 31 million doses per day and 1020 live cells per year) is a probiotic species of particular interest

(19).

L. casei is a Gram-positive, nutritionally fastidious, aciduric, facultative anaerobic, strictly fermentative rod that can be found in various environmental habitats, including raw and fermented dairy products (especially cheese) and plant materials (e.g., wine, pickle, silage, and kimchi), as well as the oral cavity, reproductive and GI tracts of humans and animals (33). The species is well characterized on the genetic level. Multilocus sequence typing has revealed the divergence of three major lineages of L. casei approximately 1.5 million years ago (8). Additionally, a detailed analysis of the L. casei ATCC 334 genome

44 has been published (9). Finally, comparative analysis of 17 L. casei genomes has revealed an average genome content of 2,780 genes and that the species has a predicted core genome of

1,715 genes, indicating that approximately 38% of the genes are variable in an average L. casei genome (6). These results demonstrate that significant strain-to-strain variation exists within this species and that significant strain-to-strain differences in probiotic efficacy and effects are likely.

The primary probiotic properties ascribed to strains of L. casei are related to immune health benefits. Studies with human peripheral blood mononuclear cells have demonstrated that strains of L. casei are able to modulate cytokine expression and natural killer activity

(17, 47). Additionally, human clinical studies have demonstrated that consumption of L. casei DN-114001 by elderly people increased the specific antibody responses to influenza vaccination (27) and decreased the duration of common infectious diseases, such as upper respiratory tract infections and rhinopharyngitis (5). While a number of studies have demonstrated the ability of L. casei to modulate the immune system, a detailed understanding of the mechanisms involved remains unknown.

To study factors that affect probiotic efficacy, in vitro and in vivo models have been developed. The primary advantages of in vitro models are their ability to eliminate the biological variation inherent to in vivo models and that they allow for systematic evaluation of single parameters (i.e., stomach pH). In addition, validated models can be used for relatively rapid and inexpensive screening of large number of probiotic strains. There are two commonly utilized in vitro models to study gastric passage: the simulated human intestinal microbial ecosystem (SHIME) and TNO [Netherlands Organization for Applied

45

Scientific Research] gastrointestinal model (TIM) models. SHIME is a five-step reactor that simulates the conditions present in the duodenum and jejunum, ileum, cecum and ascending colon, transverse colon, and descending colon (40). The TIM model is a computer-controlled system that simulates the conditions present in the human stomach and small intestine (39).

The primary advantage of in vivo models is that they allow for the examination of complex interactions between probiotics and the host (intestinal and immune systems) and, ideally, provide conditions that closely mimic human physiology. Mouse, rat, and pig models are widely utilized as non-primate systems for the evaluation of probiotics (7, 11,

34). The primary advantages of pigs are that they are omnivores, have similar nutritional requirements, and their GI tract (i.e., digesta transit time, intestinal villus structure, and epithelial cell types) and associated metabolic processes are quite similar to humans (44).

Collectively, the in vitro and in vivo models developed for the examination of probiotic strains provide an array of approaches to screen strains for probiotic efficacy and opportunities to examine their mechanisms of action. The aim of this study was to compare survival of four different strains of L. casei during GI passage in in vivo and in vitro models.

Additionally, the ability of one strain to alter the composition of the ileum microbiota and the organic acid composition of the ileum digesta was evaluated.

46

Materials and methods

Bacterial strains. L. casei strains included in this study are described in Table 1.

Stock cultures were maintained at -80°C in MRS broth (BD Difco, Sparks, MD) with 25%

(v/v) glycerol. Working cultures were prepared from frozen stock by two sequential transfers in MRS broth and incubations were conducted statically at 37°C for 24 h and 16-18 h, respectively.

Selection of streptomycin-rifampicin resistant L. casei derivatives. Streptomycin- rifampicin resistant derivatives of each L. casei strain were isolated essentially as previously described (36). Briefly, MRS broth supplemented with 0.6 µg/ml streptomycin (Str) (Sigma-

Aldrich, St. Louis, MO) was inoculated with working cultures (0.5% v/v) and incubated statically at 37°C until turbid growth was observed (approximately 24 h). Subsequently, the cultures were serially transferred to MRS broth with a higher Str concentration until resistance to 600 µg/ml Str was achieved. The Str-resistant derivatives were then treated in a similar manner to obtain derivatives resistant to both Str and 0.25 µg/ml rifampicin (Rif)

(Sigma-Aldrich). Cultures were serially transferred in MRS broth with 600 µg/ml Str and sequentially higher Rif concentrations up to 100 µg/ml. To confirm the identity of StrRRifR

L. casei derivatives, a minimum of three StrRRifR isolates per parental strain was analyzed by multilocus sequence typing (MLST) of three housekeeping genes (mutL, nrdD, and polA), then one of those isolates was analyzed by comparative genome hybridization (CGH) as previously described (8, 9).

47

Culture preparation for the piglet and in vitro experiments. Working cultures of the antibiotic-resistant derivatives were inoculated (0.5% v/v) into 1 L of MRS broth containing 600 µg/ml Str and 100 µg/ml Rif (MRS Str Rif) and incubated statically at 37°C for 18 h. The cultures were harvested by centrifugation at 5,000 ×g for 10 min at 25°C. The pellet was resuspended in 1 L of 0.85% NaCl (w/v) and the optical density at 600 nm (OD600) determined. A volume of washed cells (based upon the OD600) sufficient to yield a 1 L cell suspension with an OD600 of 6.0 was harvested by centrifugation at 5,000 ×g and washed with 1 L of 0.85% NaCl. The resulting pellet was suspended in 10 ml of UHT skim milk

(Gossner Foods, Logan, UT) to obtain a final concentration of 1010 - 1011 CFU/ml. The pH of the milk containing the culture was adjusted to 4.2 by addition of L-lactic acid (Sigma-

Aldrich). For the in vitro experiments, 5 ml of UHT skim milk was utilized. The acidified milk cultures were stored at 4°C overnight and the cultures were enumerated on MRS Str Rif agar just prior to addition to the in vitro model. For the piglet trial, the acidified milk cultures were divided into 10 ml aliquots and stored at 4°C until fed (less than 8 days). The culture was enumerated daily on MRS Str Rif agar prior to being fed to the piglets.

The in vitro GI passage model. The in vitro GI passage model consisted of a 200 ml fermentation vessel, prepared by the Glass Shop at the University of Wisconsin-Madison

Department of Chemistry, and equipment that allowed for control of temperature, pH, and oxidation-reduction potential (Eh). An anaerobic environment was maintained by flushing the fermentation vessel with filter sterilized (0.1 µm) anaerobic mix (95% N2, 5% CO2) at a flow rate of 0.1 L/min. The pH was maintained using an Extech Model 48PH2 pH controller

(Extech Instruments, Waltham, MA) and a 405-DPAS-SC-K8S pH electrode (Mettler-

48

Toledo, Bedford, MA) via the automatic addition of either 0.1N HCl or 0.1N NaOH. The Eh was maintained below -50 mV using a Jenco Model 3676 ORP controller (Jenco Instruments,

San Diego, CA) and a Pt4805-DPAS-SC-K8S redox electrode (Mettler-Toledo) via automatic addition of 1.5% TiCl3 (Fisher Scientific, Fair Lawn, NJ) in 0.2 M citrate which had been neutralized to pH 7.0 with sodium carbonate (60). Temperature was maintained at 37°C using a Traceable temperature controller (Control Company,

Friendswood, TX). Mixing was accomplished via an E-STEM standard mini-stirrer (Thermo

Fisher Scientific, Dubuque, IA) set at 500 rpm.

Prior to the addition of the acidified milk culture, 25 ml of UHT skim milk, 300 mg of pepsin (Sigma-Aldrich, catalog no. P7000) and 75 ml of an ileal delivery media were added into the fermentation vessel. The ileal delivery media consisted of 0.2 g D-galactose

(BD Difco), 0.47 g pectin (TCI America, Portland, OR), 0.47 g xylan (TCI America), 0.47 g arabinogalactan (TCI America), 0.47 g amylopectin (TCI America), 3.92 g starch (Alfa

Aesar, Ward Hill, MA), 0.25 g K2HPO4.3H2O, 0.45 g NaCl, 0.0005 g FeSO4.7H2O, 0.05 g

MgSO4.7H2O, 0.045 g CaCl2.2H2O, 0.04 g cysteine.HCl (Sigma-Aldrich), 0.1 ml Tween-80

(Sigma-Aldrich), and 1X RPMI 1640 mixture (Sigma-Aldrich) (56). Simulated GI passage was initiated by adding 5 ml of an acidified milk culture and adjusting pH to 2.5 with

HCl.

The conditions and times utilized to simulate the different compartments of the upper

GI tract were based upon those described for SHIME and TIM (39, 40). The conditions and times for each compartment were: stomach, pH 2.5 with 0.3% pepsin for 1.5 h; duodenum, pH 6.5 with 1X bile/pancreatin solution for 1.5 h; jejunum, pH 6.8 with a 0.5X

49 bile/pancreatin solution for 1.5 h; and ileum, pH 7.2 with a 0.25X bile/pancreatin solution for

3 h. Ten ml of a 10X bile/pancreatin solution was added to obtain the 1X bile/pancreatin concentration in the duodenum stage of the GI passage model. The 10X bile/pancreatin solution contained 4.2% bile salts (Sigma-Aldrich, catalog no. B8756), 3.1% NaHCO3

(Fisher Scientific), and 0.49% pancreatin (MP Biomedicals, Solon, OH) consisting of trypsin, chymotrypsin, elastase, and carboxypeptidases A and B. The bile/pancreatin concentration in the subsequent stages of the GI passage model was adjusted by dilution with 0.63%

NaHCO3.

Piglet feeding trials. The piglet diet, which is detailed in Tables S1 and S2, was based upon the diet of a typical Western elementary age child and was developed by Monica

Theis, RD (University of Wisconsin-Madison, Department of Food Science) and Dr. Thomas

Crenshaw (University of Wisconsin-Madison, Department of Animal Sciences).

Three litters of piglets from Duroc and Yorkshire-Landrace crosses that had been born on the same day and that had at least five piglets were obtained directly after weaning; five groups comprised of three piglets, with each piglet coming from a different litter, were formed. Water was provided ad libitum and the housing temperature was maintained at

26.7°C by a heat lamp. After 18 days of nursing, weaned pigs were individually fed a humanized diet meal at 7 AM, 12 PM, and 6 PM daily for 14 days. After the two-week period, four groups were also fed 10 ml of an acidified milk culture containing one of the four StrRRifR L. casei strains daily for seven days, while the fifth (control) group was fed 10 ml of acidified milk without added L. casei. The piglets were weighed at 18, 28, and 35 days to adjust the amount of diet fed (Table S1). Six hours after the last dose, the piglets were

50 sacrificed by lethal injection with Beuthanasia-D into their jugular veins. The time allowed after the last dose prior to sacrificing the piglets was based upon a preliminary study, which examined the time required for a meal to reach the distal ileum (data not shown). The piglet intestinal tracts were surgically removed and divided into 0.5 m sections beginning at the proximal end of the small intestine.

The digesta from each section was recovered by manual distension and serially diluted to 10-5 in 0.85% NaCl. Intestinal wall samples (2 cm2) from the four most distal ileum sections were removed, washed twice in 10 ml of 0.85% NaCl, homogenized using a

PT 10/35 homogenizer (Brinkmann Instruments, Delran, NJ) and then serially diluted in

0.85% NaCl. The 10-1 and 10-2 dilutions were plated using the standard pour-plated method, while 10-2 to 10-5 dilutions were plated using the drop-plated method (38). MRS Str Rif agar plates supplemented with 50 U/ml nystatin to inhibit the fungal growth (Sigma-Aldrich) were utilized to select for the StrRRifR L. casei strains. Agar plates were incubated at 37°C for 48 h prior to enumeration. The 10-1 dilution of the four distal ileum samples from the control and L. casei 32G-fed piglets was stored at -20oC for further analysis.

To confirm the identity of the strains recovered, 25 colonies were randomly picked from the MRS Str Rif agar plates collected from each group. These cultures were examined for catalase activity (23); the catalase-negative cultures were identified by 16S rRNA sequencing (45). The L. casei cultures were compared to the fed strains by pulsed field gel electrophoresis (PFGE) as described previously (8).

Determination of lactic acid content in the ileum digesta. The amount of D- and

L-lactic acid in digesta recovered from the distal ileum section of control pigs and pigs fed L.

51 casei 32G was determined using D-Lactic acid/L-lactic acid kit from R-Biopharm

(Darmstadt, Switzerland). A 0.9 ml sample of digesta was centrifuged at 20,000 ×g for 1 h and the supernatant was recovered. Proteins present in the supernatant were precipitated using the Carrez clarification reagent (85 mM K4[Fe(CN)6] and 250 mM ZnSO4) as described for the D-Lactic acid/L-lactic acid kit and the pH adjusted to 8.0 using 1N NaOH.

Subsequently, D- and L-lactate were quantified as directed by the supplier, except that the total volume of the assay was decreased from 3 ml to 600 µl, while maintaining the proportions described in the manufacturer’s instructions for each reagent.

Automated ribosomal intergenic spacer analysis (ARISA). Total DNA from 0.2 ml of digesta, recovered from control piglets and piglets fed StrRRifR 32G, was isolated using the QIAamp DNA Stool Mini Kit (Qiagen Sciences, MD). ARISA-PCR was conducted using primer 1406f, 5' - TGY ACA CAC CGC CCG T - 3', which was labeled with phosphoramidite dye 5-FAM, and primer 23Sr, 5' - GGG TTB CCC CAT TCR G - 3' (22).

PCR amplification was performed using iProof High-Fidelity DNA polymerase (Bio-Rad

Laboratories, Hercules, CA) with an iCycler Thermal Cycler (Bio-Rad Laboratories). The

PCR conditions utilized for amplification of the 16S-23S spacer region were: initial denaturation at 98°C for 3 min; 35 cycles of 98°C for 10 sec, 55°C for 30 sec, and 72°C for

30 sec; final extension at 72°C for 1 min; and holding at 4°C. A 50 µl reaction with 150 ng of genomic DNA was prepared according to iProof High-Fidelity DNA polymerase directions. One µl of PCR products, 0.4 µl of custom 100- to 2,000-bp standard labeled with

Rhodamine X (Bioventures, Murfreesboro, TN), and 10 µl of highly deionized formamide

(Applied Biosystems, Foster City, CA) were submitted to the University of Wisconsin-

52

Madison Biotechnology Center for capillary electrophoresis in an ABI 3700 Genetic

Analyzer (31). Amplicon sizes were determined by comparison to the internal size standard.

Peak area, which is proportional to DNA quantity, was calculated using PeakScanner

(Applied Biosystems). Peaks with greater than 1,000 fluorescence units, which were between 300-1,000 bp, were included in the ARISA profiles.

Microbiota analysis by pyrosequencing. The partial 16S rRNA genes were sequenced by Roche-454 GS FLX Titanium technology at University of Nebraska-Lincoln,

Core for Applied Genomics and Ecology (CAGE) as previously described (2). Briefly, the

V1-V2 region was amplified using bar-coded fusion primers with the Roche-454 A or B titanium sequencing adapters (shown in italics), followed by a unique 8-base barcode sequence (B) and finally the 3' ends of primer A-8FM (5' - CCA TCT CAT CCC TGC GTG

TCT CCG ACT CAG BBB BBB BBA GAG TTT GAT CMT GGC TCA G - 3') and of primer B-357R (5' - CCT ATC CCC TGT GTG CCT TGG CAG TCT CAG BBB BBB BBC

TGC TGC CTY CCG TA - 3'). All PCR reactions were quality-controlled for amplicon saturation by gel electrophoresis; band intensity was quantified against standards using

GeneTools software (Syngene). The amplicons from all reactions were pooled in equal amounts and gel purified. The resulting products were quantified using PicoGreen

(Invitrogen) and a Qubit fluorometer (Invitrogen) before sequencing. The data processing pipeline removed low-quality reads (i) that did not completely match the PCR primer and barcode, (ii) that were shorter than 200 bp or longer than 500 bp in length, (iii) that contained more than two undetermined nucleotides (N), and (iv) had an average quality score over 20.

After filtering, there were 68,313 reads obtained from 12 samples (3,356-8,514 and 1,448-

53

17,318 reads in the digesta and tissue samples, respectively). Each read was trimmed to remove 3' adapter and primer sequences and was parsed by a barcode.

The taxonomic status was assigned to each read using a parallelized version

CLASSIFIER (59). At the standard threshold of 0.8, reads were classified down to the lowest level until the score < 0.8, at which point reads are classified as ―unclassified‖ at the next-higher taxonomic rank. The output data from CLASSIFIER, which showed the numbers of reads in each genus, were normalized across the same type of samples using rarefaction in QIIME (10).

Statistical analysis. The number of intestinal sections was normalized to 19 for all piglets. L. casei numbers are presented as CFU/section and CFU/cm2 with the standard error of mean (SE) for digesta and tissue samples, respectively. Statistically significant differences in bacterial numbers and log reductions (the number present in acidified milk minus the total recovered from GI model or piglets) among strains were determined by Analysis of Variance

(ANOVA; SAS version 9.1.3). The lactic acid content in digesta recovered from the distal ileum section of control and L. casei 32G-fed piglets was also compared by ANOVA.

For the pyrosequencing data, statistical differences between samples were compared by between group analysis (Monte-Carlo test) in package ade4 (18) of R version 2.14.0 (49) as described by de Carcer et al. (14). The predominant genera that are reduced or increased were determined by correspondence analysis in package ade4 of R version 2.14.0 as described by de Carcer et al. (14). Normalized data were presented as mean ± SE.

54

Results

Survival of L. casei strains during simulated GI passage. Characterization of L. casei StrRRifR derivatives by MLST and CGH confirmed they were indistinguishable from their respective parental strains (data not shown), and the ability of each strain to survive passage through a single vessel in vitro GI model is depicted in Figure 1 and Table S3. The most significant reduction in viable counts occurred during the simulated stomach conditions with reductions of 4.9, 4.3, 3.8, and 3.5 log CFU for strains 32G, ATCC 334, UWP, and DN-

114001, respectively. Significant strain-to-strain variation was observed in response to the simulated duodenum conditions with strain 32G exhibiting a 1.2 log CFU increase in viability, while a 1.4 log CFU decrease in viability was observed with strain DN-114001. In general, the strains examined increased in number slightly while exposed to the simulated conditions of the jejunum and ileum (Figure 1). Reduction in viable counts after the simulated ileum for the four strains examined is presented in Table 2. The survival of L. casei DN-114001 was significantly (p < 0.05) lower than that of the other strains examined.

Evaluation of L. casei strains in the piglet model. The weight of the piglets at 18,

28, and 35 days confirmed that the piglets remained healthy throughout the duration of the study. PFGE analysis of 25 StrRRifR CFU isolated from the digesta of each piglet indicated that 92%, 67%, 83%, and 89% of the isolates from piglets fed 32G, ATCC334, UWP, or DN-

114001, respectively, were indistinguishable from the strains fed (data not shown). These values were used to correct the MRS Str Rif counts obtained from the sections of the small intestine 6 h after consumption of the last acidified milk culture and the corrected values are

55 presented in Figure S1. For all four of the strains examined, the highest quantity of digesta was obtained from the distal section of the ileum (Table S4); this corresponded with the sections containing the highest level of recovered L. casei and likely reflected transit of the last meal. The total number of L. casei recovered from the piglet small intestines was significantly (p < 0.05) higher in the L. casei-fed piglets than in the control piglets (Table 2).

Interestingly, the L. casei isolates from the control piglets were determined to be indistinguishable from strain 32G by PFGE and likely arose as a result of cross contamination during feeding. The total number of L. casei recovered from the piglet small intestines was significantly (p < 0.05) lower in piglets fed L. casei DN-114001 than that observed with the other three L. casei strains (Table 2), while there was no significant difference in the number of microorganisms recovered from intestines of piglets fed 32G,

ATCC 334, or UWP. Similarly, the log reduction of L. casei DN-114001 was significantly

(p < 0.05) higher than that observed with 32G, ATCC 334, and UWP and the log reductions observed with 32G, ATCC 334, and UWP were not significantly different from each other.

Recovery of L. casei strains on piglet small intestine tissue samples. The corrected values for numbers of the fed strains per cm2 of tissue obtained from the last four sections of the piglet small intestines 6 h after consumption of the last acidified milk culture are presented in Figure 2. The number of L. casei recovered per cm2 of the piglet small intestine tissue samples was significantly (p < 0.05) higher in the L. casei-fed piglets than in the control piglets (Figure 2). Once again, the L. casei isolates from the control piglets were indistinguishable from strain 32G by PFGE. The low level of strain 32G present in the control piglet intestines likely is the result of cross contamination during feeding. The total

56 number of L. casei present per cm2 of the piglet small intestine tissue was significantly higher in piglets fed L. casei 32G (p < 0.05) than that observed with the other three L. casei strains examined (Figure 2).

Lactic acid content in the ileum digesta. The pH and lactic acid content of digesta from the last section of piglet small intestines obtained 6 h after ingestion of acidified milk or acidified milk plus L. casei 32G are presented in Table S5. There was no statistical difference in the pH or D-lactate content of digesta obtained from the control and L. casei

32G-fed piglets. However, a significantly (p < 0.10) higher level of L-lactate was observed in digesta obtained from the 32G-fed piglets, relatively to that observed in digesta obtained from the control piglets.

Composition of the microbiota in digesta from piglet small intestines. ARISA is based upon differences between species in the length of the spacer region between the 16S and 23S rRNA genes (22). Analysis of the genome sequences of L. casei ATCC 334 and

Zhang indicated that L. casei contains five rRNA operons. Three of the rRNA operons contain spacers of 216 bp while the other two copies contain spacers of 424 bp, due to the presence of Ala-tRNA and Ile-tRNA genes. Therefore, given the location of the primer annealing sites, PCR products of 502 and 710 bp were expected from L. casei strains. These amplicons were confirmed utilizing 32G genomic DNA.

Results of ARISA analyses of the microbiota present in the last section of small intestine of control and L. casei 32G-fed piglets are presented in Figure S2 and Table S6.

Twenty-six peaks ranging in length between 300-1,000 bp with a baseline of 1,000 fluorescence units were selected to create the ARISA profile for each piglet and the peak

57 quantities observed in a group of three piglets were averaged (Table S6). Results of these analyses show that 56 ± 11 % of spacer regions amplified from the digesta of L. casei 32G- fed piglets were the fragments (502 and 710 bp) expected from L. casei (Figure S2, Table

S6), indicating that L. casei was the dominant organism in this microbiota. In addition to changes in the level of L. casei, the ARISA results indicated that significant differences existed between the ileum microbiota of control versus piglets fed 32G (Figure S2, Table

S6).

To further investigate the differences between the ileum microbiota of control- and L. casei 32G-fed piglets, the compositions of these microbiotas were analyzed by 16S rRNA pyrosequencing. A total of 68,313 filtered reads were obtained from the 12 samples; the number of reads varied from 1,448 to 17,318 per sample, with an average of 5,693 reads per sample. This sequencing depth allowed for identification of components of the microbiotas at the level of genus. After the taxonomic status of each read was assigned by CLASSIFIER,

11 phyla, 18 classes, 38 orders, 82 families, and 190 genera were detected in the overall data set (data not shown). Between group analysis was conducted to analyze the differences between the control and 32G-fed piglet complete digesta and tissue data sets. This analysis demonstrated that the ileum digesta microbiota (P = 0.04; Figure 3), but not the ileum tissue microbiota (P = 0.28; Figure 4), differed significantly between the control and 32G-fed piglets. Correspondence analysis was utilized to determine if supplementation of the humanized piglet diet with L. casei 32G resulted in a significant shift in the taxa present in the digesta and tissue ileum samples. These differences were significant (p < 0.05) for 4 phyla, 8 classes, 9 orders, 18 families, and 17 genera between the control and 32G-fed piglet

58 ileum digesta samples, while significant differences in 0 phyla, 3 classes, 2 orders, 13 families, and 14 genera were observed between the control and 32G-fed piglet ileum tissue samples (Tables 3 and 4).

The dominant genera in rank order (> 5% of the total microbiota) of the ileum digesta microbiota in the control piglets were Turicibacter, Clostridium, Peptostreptococcaceae

Incertae Sedis, and Lactobacillus, which accounted for 90.12% of the total microbiota (Table

S7), while the dominant genera in rank order of the ileum digesta microbiota in the 32G-fed piglets were Lactobacillus, Peptostreptococcaceae Incertae Sedis, and Actinobacillus, which accounted for 85.59% of the total microbiota (Table S7). Comparison of the dominant components of the ileum digesta microbiotas in the control versus 32G-fed piglets by correspondence analysis (Table 4), revealed statistically significant reductions in the preponderance of Turicibacter (30.05 vs. 0.08%; p = 0.001) and Clostridium (22.96 vs.

0.08%; p = 0.001) and increases in the percentages of Actinobacillus (< 0.0005 vs.15.83%; p

= 0.005) and Lactobacillus (15.86 vs. 38.38%; p = 0.027). In addition, statistically significant differences (p < 0.05) were observed in the preponderance of minor components of these microbiotas, including Acinetobacter, Actinomyces, Bacteriodes, Corynebacterium,

Enhydrobacter, Flavimonas, Gemella, Heliobacter, Lachnospiracease Inserta Sedis,

Microvirgula, Novosphingobium, Pasteurella, and Sphingobium (Table 4).

The dominant genera in rank order (> 5% of the total microbiota) in the control piglets ileum tissue microbiota were Acinetobacter, Leuconostoc, Weissella, Pseudomonas, and Comamonas, which accounted for 73.78% of the total microbiota (Table S7), while the dominant genera in rank order of the ileum tissue microbiota in the 32G-fed piglets were

59

Exiguobacterium, Acinetobacter, Aeromonas, and Sphingobacterium, which accounted for

72.79% of the total microbiota (Table S7). Comparison of the dominant components of the ileum tissue microbiotas in the control versus 32G-fed piglets by correspondence analysis

(Table 4) revealed statistically significant increases in the percentages of Aeromonas (0.05 vs. 16.15%; p = 0.041) and Sphingobacterium (0.05 vs. 10.18%; p = 0.043). In addition, statistically significant differences (p < 0.05) were observed in the preponderance of minor components of these microbiotas, including Achromobacter, Acidovorax, Alistipes,

Brevundimonas, Clostridium, Delftia, Elizabethkingia, Ochrobactrum, Pasteuriaceae

Incertae Sedis, Pedobacter, Phenylobacterium, and Staphylococcus (Table 4).

60

Discussion

The intent of this study was to screen potential probiotic strains of L. casei for attributes commonly believed to be important for immunomodulatory probiotic efficacy.

Attributes thought to be of importance for immunomodulatory probiotics include: 1) highly resistant to the conditions present in the stomach and small intestine (low pH and bile acids);

2) ability to persist in the intestine; 3) ability to adhere to the gut epithelium; and 4) ability to interact with or signal to immune cells associated with the gut (24). It is likely that there are significant interactions between these attributes. For example, strains that strongly adhere to the gut epithelium are also likely to exhibit extended persistence in the gut. Here, we compared the ability of three potential probiotic strains and one commercially used probiotic strain to survive the conditions present in the upper GI tract, adhere to the gut epithelium of the ileum of piglets, and modify the piglet ileum digesta and tissue microbiotas.

Resistance to the conditions present in stomach and small intestine was evaluated in both an in vitro model and in piglets. Results from the in vitro model indicated that the log reduction between the initial numbers and the end of the ileum section of the model ranged from 3.2 to 4.4 log CFU (Table 2). The loss of viability was highest under the stomach simulated conditions for all strains evaluated, with significantly less loss of viability occurring in the simulated small intestine conditions (Table S3). L. casei DN-114001 exhibited significantly greater reduction in viability compared to the other three strains evaluated, and also differed in that viability decreased during incubation under conditions that simulate the duodenum (Figure 1 and Table S3). These results suggest that L. casei DN-

61

114001 is more sensitive to bile than the other strains examined, as bile is the primary bacterial inhibitor in the duodenum (29). In contrast, the other three strains increased in number during incubation under conditions that simulate the small intestine (Figure 1).

Given the relatively rapid increase in numbers, the most likely explanation is that these increases reflect recovery of injured cells, rather than growth. Overall, the in vitro model suggested three of four strains tested would survive gastric passage and reach the ileum.

To explore the strength of these predictions in vivo, all four strains were also evaluated in piglets. The log reductions between the number of organisms present in acidified milk and total recovered from the piglet small intestine ranged from 1.1 to 3.1 log

CFU (Table 2), with a significantly (p < 0.05) greater decline in L. casei DN-114001 viability versus the other three strains evaluated. The in vitro model resulted 1.3 to 2.2 log greater reductions in viability compared to that observed in the piglet model, suggesting that the conditions utilized in the in vitro model were more restrictive than those present in the piglets. Oozeer, et al. (41) examined survival of L. casei DN-114001 during GI passage in human subjects, and observed a 1.7 log CFU reduction between the number of organisms fed and those recovered at the ileo-cecal junction. The difference observed between survival of

L. casei DN-114001 during GI transit to the ileum in humanized diet fed piglets and intubated fasting humans likely reflects the considerable differences in methodology utilized to assess survival during GI passage. L. casei 32G, ATCC 334, and UWP retained viability during passage through in vitro and in vivo models of the human GI tract at a significantly (p

< 0.05) higher level than L. casei DN-114001, an industrially used probiotic strain.

62

The ability to adhere to ileum epithelium is of great significance for immunomodulatory probiotics, as this section of the small intestine is known to be an important site for interactions between the gut microbiota and the gut associated lymphoid tissue (GALT) (46). The last four 0.5 m sections of the piglet ileum selected for evaluation of L. casei adherence in this study contained a contiguous Peyer’s patch that ran from the ileo-cecal junction through these intestinal sections (data not shown). Results from this analysis indicate that L. casei 32G was present at significantly (p < 0.05) higher levels on these tissue samples than the other three strains examined (Figure 2).

The attributes of L. casei 32G that resulted in higher adherence of this strain to the ileum epithelium are unknown. However, surface-layer proteins, LPXTG-motif proteins, anchorless housekeeping proteins, transporter proteins, and pili have been reported to have roles in adherence of other lactobacilli (1, 35). Nonetheless, the ability of L. casei 32G to adhere to the piglet ileum epithelium suggests this strain may have immunomodulatory properties that are desirable in probiotic strains.

Metabolically active microorganisms may influence the ileum microbiota via a number of mechanisms including: 1) indirectly via immune modulation by metabolites (i.e., bacteriocins) or cell envelope proteins (1, 37); 2) directly via competition for growth substrates (30, 51) and the production of microbial inhibitors such as organic acids and bacteriocins (13, 21). Since L-lactic acid is the primary metabolic end product of L. casei

(32, 54), the pH and lactate concentration of digesta from the ileo-cecal junction were determined to assess L. casei 32G metabolic status. While no difference in pH was observed, the L-lactate concentration was significantly (p < 0.10) higher in the 32G-fed piglets than in

63 the control piglets (Table S5), suggesting that L. casei 32G is metabolically active in the piglet ileum.

The microbiota of the GI tract is essential for the normal mammalian physiology (52).

The microbiota of the ileum is of particular interest, as the ileum is thought to be an important site for modulation of immune function by the GI microbiota (16). Two previous investigations have examined the ileum microbiota of piglets at the genera level utilizing 16S rRNA sequencing (50, 55). Rettedal et al. (50) examined the influence of chlorotetracycline on the ileum piglet microbiota and reported that the dominant genera rank order (> 5% of the total microbiota) in their control piglet digesta (lumen) microbiota were Lactobacilllus,

Clostridiales (order level identification), and Turicibacter. They also reported that compositions of the mucous and digesta microbiota were similar, but the methodology used to separate mucous and digesta allowed for substantial contamination of the mucous sample with digesta. Vahjen et al. (55) examined the influence of ZnO on the ileum piglet microbiota, and noted that the dominant genera rank order of their control piglet digesta were

Lactobacillus, Weissella, and Sarcina (order Clostridiales). In the current study, the dominant genera rank order of the ileum digesta microbiota of the control piglets were

Turicibacter, Clostridium (order Clostridiales), Peptostreptococcaceae Incertae Sedis (order

Clostridiales), and Lactobacillus (Table S7). The commonalities among these three studies are that Lactobacillus and a member of order Clostridiales (genera identified include

Sarcina, Clostridium, and Peptostreptococcaceae Incertae Sedis) are dominant members of the piglet ileal microbiota. Additionally, two of the three studies identified Turicibacter as a dominant member of the ileum digesta microbiota. The differences in the ileum microbiota

64 in the control piglets between these three studies are likely due to differences in piglet genetics, diets (humanized diet versus commercial piglet diets), and housing environments

(50).

Between group analysis showed feeding with L. casei 32G resulted in a significant change in the ileum digesta microbiota, but not in the tissue adherent microbiota. However, correspondence analysis of these data sets demonstrated that significant changes occurred in the dominant genera in both the digesta and tissue samples (Table 4). Examples include greater than 2-log decreases (relative to the control) in the levels of Clostridium and

Turicibacter in the 32G-fed piglet ileum digesta samples, as well as greater than 2-log increases in the levels of Aeromonas and Sphingobacterium in the 32G-fed piglet ileum tissue samples (Table 4). These changes are not simply due to dilution resulting from the feeding of approximately 1010 of 32G, since levels of other members of the piglet ileum microbiota (e.g. Peptostreptococcaceae Incertae Sedis, a dominant component of the digesta commensal microbiota) did not decrease significantly (Table S7). These results indicate that feeding of L. casei 32G resulted in a microbe-specific restructuring of the commensal ileum microbiotas. This observation has both broad theoretical implications and 32G-specific practical implications. The theoretical implications relate to the selection of immunomodulatory probiotic strains, which typically is based upon their ability to directly influence the expression of various immune related genes (i.e., cytokines). However, our results suggest that immunomodulatory probiotics could also act indirectly, wherein consumption of a probiotic results in restructuring of the ileum microbiota, whose components are in turn responsible for modulating the immune system. For example, if a

65 probiotic altered the ileum microbiota toward higher levels of microorganisms with lipopolysaccharide, the net result could be increased inflammation, although probiotic itself was not directly pro-inflammatory.

Finally, the L. casei 32G-induced reduction in the level of Clostridium may have practical implications, as Clostridium difficile infection is a serious medical condition that has been shown, in one clinical trial, to be prevented by administration of a probiotic preparation that contained L. casei (58). Specific microbial inhibitors that may account for these changes include 32G-produced antimicrobials (i.e., bacteriocins and organic acids) and

32G-induced piglet antimicrobials (i.e., defensins, phospholipases, C-type lectins, secretory

IgA and host-defense related ribonucleases) (3). Future research will be directed at understanding the mechanisms responsible for these 32G-induced changes in the commensal microbiota and the potential of 32G for the prevention of C. difficile infections.

66

Conclusions

Four strains of L. casei were evaluated for survival in an in vitro GI passage model and in piglets. The in vitro and piglet models yielded similar rank orders of survival of the four strains during GI passage, with L. casei DN-114001 consistently exhibiting the lowest survival. These results suggest that the in vitro model has utility in screening potential probiotic strains. L. casei 32G was among the strains exhibiting highest survival through GI passage, adhered to the ileum epithelial tissue at the highest level, was metabolically active in the ileum, and altered the microbiota composition of the ileum. Collectively, these findings suggest L. casei 32G has potential to serve as a probiotic with anti-infective and immunomodulatory properties.

67

Acknowledgements

This project was supported by Dupont Inc., the United States Department of

Agriculture, and by a Thai Government Ministry of Science and Technology scholarship for

K. Tandee. We appreciate the technical contributions from Dr. Hui Cai, Dr. Ekkarat

Phrommao, Busra Aktas, Kurt Selle, and Lulu Hoza, and from students from the Department of Animal and Food Science, University of Wisconsin-River Falls. The bioreactor glassware was produced by Tracy Drier at Department of Chemistry, University of Wisconsin-

Madison. We also thank Dr. Jaehyong Kim and Nina Murray from the University of

Nebraska and Dr. Eline Klaassens, CSIRO Livestock Industries-Australia, for assistance with the microbiota data analysis. Peggy Steele, a member of Steele’s family, is an employee of

DuPont Inc.

68

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Figure 1. Survival of Lactobacillus casei 32G (—□––), ATCC 334 (---○---), UWP (····◊····), and DN-114001 (– –Δ– –) in the different compartments of in vitro GI model.

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Figure 2. Detection of Lactobacillus casei 32G (—□––), ATCC 334 (---○---), UWP (····◊····), DN-114001 (– –Δ– –), and no added L. casei (—●—) on the distal ileum-derived tissue sections obtained 6 h after ingestion of acidified milk containing these strains.

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Figure 3. Discrimination between ileum digesta microbiotas of piglets fed acidified milk (control) and acidified milk plus Lactobacillus casei 32G. The diagram shows the single axis result of between group analysis applied to correspondence analysis. The five most dominant genera present in either the control (Turicibacter, Clostridium, Acinetobacter, Actinomyces, and Gemella) or 32G (Lactobacillus, Actinobacillus, Peptostreptococcaceae Incertae Sedis, Escherichia, Vellonella) digesta microbiotas have been included. Error bars are used to represent genus coordinates from the correspondence analysis.

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Figure 4. Discrimination between ileum tissue microbiotas of piglets fed acidified milk (control) and acidified milk plus Lactobacillus casei 32G. The diagram shows the single axis result of between group analysis applied to correspondence analysis. The five most dominant genera present in either the control (Acinetobacter, Weissella, Leuconostoc, Lactococcus and Acidovorax) or 32G (Exiguobacterium, Aeromonas, Sphingobacterium, Brevundimonas, Pasteuriaceae Incertae Sedis) tissue microbiotas have been included. Error bars are used to represent genus coordinates from the correspondence analysis.

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Table 1. Origins and references for Lactobacillus casei strains utilized in this study. Strain Ecological niche of isolation Reference 32G Corn silage, WI, USA Cai et al. (2007) ATCC 334 Swiss-type cheese, USA Chen et al. (2000) UWP Unknown UW culture collection DN-114001 Commercial yogurt, USA Cai et al. (2007)

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Table 2. Log reductions of Lactobacillus casei strains in an in vitro GI model and during transit to the piglet small intestine. Log reductions Strain in vitro GI model1 Piglet small intestines2 32G 3.2 ± 0.3b 1.6 ± 0.3b ATCC 334 3.5 ± 0.5b 1.4 ± 0.3b UWP 3.3 ± 0.1b 1.1 ± 0.4b DN-114001 4.4 ± 0.2a 3.1 ± 0.7a 1Log reductions were calculated by subtracting a number in the ileum stage of the in vitro GI model from the number in the acidified milk culture. Values in the same column with different letters differ statistically (p < 0.05). 2Log reductions were calculated by subtracting the total number of colonies from the piglet digesta recovered on MRS Str Rif agar incubated at 37°C for 48 h from the number of organisms in the acidified milk culture. The total numbers of L. casei recovered from the piglets fed 32G, ATCC334, UWP, DN-114001, and control piglets were 9.9 ± 0.3, 9.8 ± 0.5, 9.8 ± 0.6, 8.3 ± 1.6, and 5.9 ± 1.1 log CFU, respectively. Values in the same column with different letters differ statistically (p < 0.05).

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Table 3. Bacterial phyla, classes, orders, and families that differ significantly (P < 0.05) in either the piglet ileum digesta and/or tissue samples between the piglets fed either acidified milk (control) or acidified milk plus Lactobacillus casei 32G. Percentage (mean ± SE) Name Digestaa Tissueb Control 32G p-value Control 32G p-value Phyla † 0.53 ± 0.26 0.10 ± 0.03 0.026 0.35 ± 0.14 0.28 ± 0.09 0.330 Cyanobacteria† 0.08 ± 0.05 0.00 ± 0.00 0.035 0.18 ± 0.04 0.18 ± 0.18 0.500 Firmicutes† 96.60 ± 0.50 62.85 ± 11.16 0.001 22.67 ± 17.84 31.95 ± 24.71 0.330 Proteobacteria† 2.45 ± 0.53 36.54 ± 11.10 0.001 66.31 ± 21.59 45.94 ± 16.13 0.180 Classes Actinobacteria† 0.47 ± 0.25 0.10 ± 0.04 0.033 0.27 ±0.21 0.35 ± 0.24 0.350 § 0.15 ± 0.04 0.09 ± 0.05 0.120 1.39 ± 0.29 2.93 ± 1.12 0.045 † 0.40 ± 0.07 0.15 ± 0.04 0.001 13.09 ± 4.81 6.00 ± 3.46 0.068 Clostridia† 50.49 ± 7.89 28.47 ± 11.26 0.014 8.58 ± 5.99 3.08 ± 1.80 0.170 Cyanobacteria† 0.06 ± 0.04 0.01 ± 0.01 0.049 0.12 ± 0.07 0.08 ± 0.08 0.340 Deltaproteobacteria§ 0.00 ± 0.00 0.01 ± 0.01 0.140 0.35 ± 0.20 0.00 ± 0.00 0.012 Epsilonproteobacteria† 0.02 ± 0.01 0.11 ± 0.02 0.001 1.66 ± 1.21 0.08 ± 0.08 0.057 Erysipelotrichi† 27.02 ± 2.91 0.09 ± 0.04 0.001 0.46 ± 0.46 0.89 ± 0.83 0.310 Flavobacteria† 0.11 ± 0.03 0.06 ± 0.00 0.004 3.31 ± 1.21 11.16 ± 6.94 0.086 † 2.06 ± 0.41 36.11 ± 11.20 0.001 51.19 ± 16.06 37.68 ± 13.24 0.240 Sphingobacteria§ 0.01 ± 0.01 0.01 ± 0.01 0.620 0.12 ± 0.07 7.27 ± 4.60 0.023 Orders Burkholderiales† 0.30 ± 0.01 0.11 ± 0.02 0.001 11.32 ± 4.23 7.08 ± 4.57 0.210 Campylobacterales† 0.02 ± 0.01 0.10 ± 0.03 0.001 1.48 ± 1.10 0.04 ± 0.04 0.055 Caulobacterales§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 1.24 ± 0.65 0.014 Clostridiales† 50.04 ± 7.68 28.38 ± 11.13 0.029 8.56 ± 5.99 2.53 ± 1.36 0.110 Erysipelotrichales† 27.93 ± 2.64 0.09 ± 0.04 0.001 0.51 ± 0.45 1.01 ± 0.95 0.280 Flavobacteriales† 0.13 ± 0.02 0.03 ± 0.02 0.001 4.01 ± 1.54 12.68 ± 7.83 0.100 Neisseriales† 0.13 ± 0.06 0.04 ± 0.01 0.024 0.12 ± 0.12 0.08 ± 0.08 0.340 Pasteurellales† 0.07 ± 0.07 32.21 ±11.68 0.001 0.00 ± 0.00 0.00 ± 0.00 N/A Pseudomonadales† 1.06 ± 0.22 0.66 ± 0.10 0.012 48.93 ± 18.97 20.81 ± 8.89 0.057 Rhodocyclales† 0.03 ± 0.00 0.00 ± 0.00 0.001 0.19 ± 0.10 0.12 ± 0.07 0.230 Sphingobacteriales§ 0.01 ± 0.01 0.01 ± 0.01 0.870 0.04 ± 0.04 6.73 ± 3.95 0.047 Families Actinomycetaceae† 0.21 ± 0.10 0.02 ± 0.02 0.013 0.04 ± 0.04 0.00 ± 0.00 0.130 Aeromonadaceae§ 0.00 ± 0.00 0.01 ± 0.01 0.110 0.09 ± 0.09 10.74 ± 7.99 0.046 Alcaligenaceae† 0.02 ± 0.01 0.00 ± 0.00 0.013 0.00 ± 0.00 0.73 ± 0.67 0.084 Bacillaceae§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.13 ± 0.07 0.012 Bacteroidaceae† 0.04 ± 0.01 0.00 ± 0.00 0.001 1.68 ± 1.43 0.43 ± 0.37 0.180 Brucellaceae§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.26 ± 0.15 0.017 Caulobacteraceae§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 1.21 ± 0.67 0.011 Chloroplast†§ 0.07 ± 0.03 0.01 ± 0.01 0.008 0.13 ± 0.07 0.00 ± 0.00 0.037 Clostridiaceae†§ 25.97 ± 7.55 0.05 ± 0.03 0.001 0.13 ± 0.07 0.00 ± 0.00 0.036 Comamonadaceae† 0.23 ± 0.01 0.07 ± 0.02 0.001 8.97 ± 3.52 4.27 ± 2.41 0.085 Corynebacteriaceae† 0.04 ± 0.03 0.00 ± 0.00 0.030 0.04 ± 0.04 0.00 ± 0.00 0.130 Erysipelotrichaceae† 27.78 ± 3.08 0.09 ± 0.03 0.001 0.52 ± 0.52 0.86 ± 0.74 0.300 Flavobacteriaceae† 0.13 ± 0.03 0.04 ± 0.01 0.001 3.79 ± 1.39 11.51 ± 7.21 0.110 Helicobacteraceae† 0.00 ± 0.00 0.07 ± 0.04 0.007 1.25 ± 1.00 0.13 ± 0.07 0.083 Incertae Sedis 5§ 0.03 ± 0.02 0.02 ± 0.02 0.350 2.93 ± 1.21 0.69 ± 0.63 0.023

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Incertae Sedis XI† 0.24 ± 0.12 0.03 ± 0.02 0.013 0.00 ± 0.00 0.00 ± 0.00 N/A Incertae Sedis XIII† 0.00 ± 0.00 0.02 ± 0.01 0.013 0.00 ± 0.00 0.00 ± 0.00 N/A Lachnospiraceae† 0.60 ± 0.32 0.04 ± 0.01 0.018 5.43 ± 3.78 1.64 ± 0.85 0.130 Moraxellaceae†§ 0.91 ± 0.19 0.44 ± 0.07 0.001 39.54 ± 14.98 15.57 ± 9.77 0.045 Neisseriaceae†§ 0.10 ± 0.02 0.04 ± 0.01 0.002 0.17 ± 0.11 0.00 ± 0.00 0.042 Paenibacillaceae§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.09 ± 0.04 0.005 Pasteurellaceae† 0.07 ± 0.07 31.38 ± 11.63 0.001 0.00 ± 0.00 0.00 ± 0.00 N/A Planococcaceae§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 2.72 ± 1.10 0.002 Rhodocyclaceae† 0.03 ± 0.00 0.00 ± 0.00 0.001 0.13 ± 0.07 0.09 ± 0.09 0.340 Rikenellaceae§ 0.04 ± 0.04 0.01 ± 0.01 0.180 1.25 ± 0.64 0.13 ± 0.07 0.025 Sphingobacteriaceae§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 6.86 ± 4.47 0.033 Sphingomonadaceae† 0.09 ± 0.04 0.02 ± 0.01 0.009 1.16 ± 0.22 0.86 ± 0.80 0.320 aThe detection limit was 0.0003 and this value was used to calculate the p-value. bThe detection limit was 0.001 and this value was used to calculate the p-value. †Phyla, classes, orders, and families that differ in the ileum digesta. §Phyla, classes, orders, and families that differ in the ileum tissue.

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Table 4. Bacterial genera that differ significantly (P < 0.05) in either the piglet ileum digesta and/or tissue samples between the piglets fed either acidified milk (control) or acidified milk plus Lactobacillus casei 32G. Percentage (mean ± SE) Genus Digestaa Tissueb Control 32G p-value Control 32G p-value Achromobacter§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.55 ± 0.39 0.043 Acidovorax§ 0.11 ± 0.04 0.05 ± 0.03 0.060 2.74 ± 1.09 0.82 ± 0.34 0.021 Acinetobacter† 0.89 ± 0.14 0.54 ± 0.07 0.003 49.43 ± 18.65 19.59 ± 11.47 0.055 Actinobacillus† 0.00 ± 0.00 15.83 ± 6.75 0.005 0.00 ± 0.00 0.00 ± 0.00 N/A Actinomyces† 0.22 ± 0.12 0.00 ± 0.00 0.019 0.00 ± 0.00 0.00 ± 0.00 N/A Aeromonas§ 0.00 ± 0.00 0.02 ± 0.02 0.160 0.05 ± 0.05 16.15 ± 12.25 0.041 Alistipes§ 0.03 ± 0.03 0.00 ± 0.00 0.089 1.42 ± 0.74 0.00 ± 0.00 0.008 Bacteroides† 0.06 ± 0.03 0.00 ± 0.00 0.011 2.08 ± 1.84 0.49 ± 0.49 0.150 Brevundimonas§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 1.86 ± 0.97 0.012 Clostridium†§ 22.96 ± 7.30 0.08 ± 0.04 0.001 0.11 ± 0.05 0.00 ± 0.00 0.016 Corynebacterium† 0.14 ± 0.07 0.00 ± 0.00 0.005 0.00 ± 0.00 0.00 ± 0.00 N/A Delftia§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.22 ± 0.05 0.00 ± 0.00 0.001 Elizabethkingia§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.22 ± 0.14 0.031 Enhydrobacter† 0.05 ± 0.00 0.02 ± 0.02 0.009 0.33 ± 0.25 0.05 ± 0.05 0.110 Flavimonas† 0.03 ± 0.02 0.00 ± 0.00 0.005 0.05 ± 0.05 1.04 ± 0.81 0.061 Gemella† 0.25 ± 0.09 0.03 ± 0.02 0.003 0.00 ± 0.00 0.00 ± 0.00 N/A Helicobacter† 0.00 ± 0.00 0.11 ± 0.06 0.009 1.64 ± 1.24 0.11 ± 0.05 0.071 Lachnospiraceae 0.08 ± 0.04 0.00 ± 0.00 0.007 0.33 ± 0.33 0.22 ± 0.14 0.340 Incertae Sedis† Lactobacillus† 15.86 ± 5.70 38.28 ± 14.00 0.027 0.55 ± 0.29 0.55 ± 0.47 0.520 Microvirgula† 0.03 ± 0.02 0.00 ± 0.00 0.004 0.16 ± 0.16 0.00 ± 0.00 0.130 Novosphingobium† 0.03 ± 0.02 0.00 ± 0.00 0.005 1.31 ± 0.62 0.66 ± 0.49 0.170 Ochrobactrum§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.16 ± 0.09 0.026 Pasteurella† 0.00 ± 0.00 0.72 ± 0.29 0.001 0.00 ± 0.00 0.00 ± 0.00 N/A Pasteuriaceae 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 1.48 ± 0.85 0.039 Incertae Sedis§ Pedobacter§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.22 ± 0.11 0.017 Phenylobacterium§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.16 ± 0.09 0.026 Sphingobacterium§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.05 ± 0.05 10.18 ± 7.22 0.043 Sphingobium† 0.00 ± 0.00 0.05 ± 0.03 0.044 0.16 ± 0.16 0.27 ± 0.27 0.340 Staphylococcus§ 0.02 ± 0.02 0.00 ± 0.00 0.120 0.00 ± 0.00 0.11 ± 0.05 0.017 Turicibacter† 30.05 ± 2.74 0.08 ± 0.02 0.001 0.60 ± 0.60 1.26 ± 1.18 0.270 aThe detection limit was 0.0005 and this value was used to calculate the p-value. bThe detection limit was 0.002 and this value was used to calculate the p-value. †Genera that differ in the ileum digesta. §Genera that differ in the ileum tissue.

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Figure S1. Detection of Lactobacillus casei 32G (—□––), ATCC 334 (---○---), UWP (····◊····), DN-114001 (– –Δ– –), and no added L. casei (—●—) in the digesta obtained from the piglet small intestines 6 h after ingestion of acidified milk containing these strains.

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Figure S2. The relative quantity of 16S-23S rRNA spacer amplicons found in the digesta obtained from control (left) or Lactobacillus casei 32G fed (right) piglets. The phylotypes 502 and 710 represented the 16S-23S rRNA spacer amplicons of L. casei.

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Figure S3. Discrimination between piglet ileum digesta and tissue microbiotas. The diagram shows the single axis result of between group analysis applied to correspondence analysis. The five most dominant genera present in either the digesta (Peptostreptococcaceae Incertae Sedis, Lactobacillus, Turicibacter, Clostridium, and Actinobacillus) or tissue (Acinetobacter, Exiguobacterium, Aeromonas, Sphingobacterium, and Comamonas) microbiotas have been included. Error bars are used to represent genus coordinates from the correspondence analysis.

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Table S1. Humanized diet for 10 kg piglets per day. Food Items Amount (gram dry matter) Amount (gram as-is) Breakfast Cheerios, General Mills 38.87 40.24 2% Milk 36.45 340.61 Orange juice, Florida 19.84 169.59 Total 95.16 550.44 Lunch Chicken breast, honey glazed, 24.25 81.92 Osgar Mayer Burger bun, plain 40.36 61.80 Carrots, raw 12.61 107.79 Orange wedge 12.05 93.42 Cookies, peanut butter 20.24 21.56 2% Milk 36.45 340.61 Total 145.95 707.09 Dinner Chicken noodle soup, Campbell 44.62 340.61 Cheese, pasteurized process, 33.67 81.92 American, lowfat Bread, wheat 46.21 71.86 Graham Cracker, plain or honey 76.94 80.48 2% Milk 36.45 340.61 Total 237.88 915.48 Total daily amount 479.0 2173.0

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Table S2. Nutrient compositiona of the piglet humanized diet per kg. Nutrients Units Dry Matter As-is Macronutrients Metabolizable energy kcal/kg 4261 938 Moisture % 0.00 78.0 Protein % 22.4 4.9 Fat % 11.3 2.5 Carbohydrates % 56.0 12.3 Fiber % 3.74 0.82 Ash % 6.6 1.45 A IU/kg 46351 10201 D IU/kg 1032 227 E IU/kg 5.6 1.2 K IU/kg 0.06 0.01 Biotin (B7) mg/kg N/A N/A mg/kg 430 95 Folacin (B9) mg/kg 1.35 0.30 Niacin (B3) mg/kg 51.1 11.2 Pantothenic acid (B5) mg/kg 13.8 3.0 Riboflavin (B2) mg/kg 7.9 1.7 Thiamin (B1) mg/kg 4.57 1.00 Pyridoxine (B6) mg/kg 3.6 0.8 Cobalamin (B12) mg/kg 0.016 0.004 Minerals Ca % 0.47 0.10 P % 0.52 0.11 Na % 1.45 0.32 Cl % N/A N/A K % 0.74 0.16 Mg % 0.07 0.02 S % N/A N/A Cu mg/kg 2.4 0.5 I mg/kg N/A N/A Fe mg/kg 49 10.9 Mn mg/kg 7.7 1.69 Se mg/kg 0.20 0.04 Zn mg/kg 33.3 7.3

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Amino acids Arg % 0.49 0.11 His % 0.27 0.06 Ile % 0.58 0.13 Leu % 1.05 0.23 Lys % 0.66 0.15 TSA % 0.61 0.13 TAA % 1.05 0.23 Thr % 0.40 0.09 Trp % 0.15 0.03 Val % 0.69 0.15 Tau % 0.00 0.00 aNutrient concentrations calculated from the food nutrition compositions derived from the USDA Nutrient Data Laboratory. USDA Food Search for Windows Version 1.0, Database version SR-19 (www.nal.usda.gov/fnic/foodcomp).

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Table S3. Log reductions of four Lactobacillus casei strains in each compartment of the in vitro GI model. Log reductions* Strain Stomach Duodenum Jejunum Ileum 32G 4.9 ± 0.7a 3.7 ± 0.7a 3.3 ± 0.4a 3.2 ± 0.4a ATCC 334 4.3 ± 1.1a 4.7 ± 0.5a 3.6 ± 0.5ab 3.5 ± 0.5a UWP 3.8 ± 0.2a 3.7 ± 0.4a 3.4 ± 0.2a 3.3 ± 0.2a DN-114001 3.5 ± 0.6a 4.9 ± 0.5a 4.5 ± 0.4b 4.4 ± 0.2b *Log reductions were calculated by subtracting the number in stomach, duodenum, jejunum, or ileum from the number in acidified milk. Values in the same column with different letters differ statistically (p < 0.05).

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Table S4. Normalized weights by intestinal section of piglet digesta. Digesta weight (g) Section Control 32G ATCC 334 UW DN-114001 1 0.8 ± 0.3 2.0 ± 2.6 2.2 ± 1.8 0.8 ± 0.2 0.4 ± 0.4 2 5.4 ± 4.3 2.4 ± 2.8 3.5 ± 2.1 2.4 ± 3.0 2.5 ± 1.4 3 3.2 ± 1.0 1.7 ± 0.8 2.9 ± 2.5 2.5 ± 0.5 3.2 ± 2.2 4 3.9 ± 0.6 1.7 ± 0.8 4.1 ± 1.5 3.9 ± 0.4 3.0 ± 1.1 5 4.2 ± 0.5 2.2 ± 0.6 4.8 ± 2.1 5.1 ± 0.4 3.4 ± 1.9 6 4.2 ± 0.2 2.6 ± 0.8 5.4 ± 2.2 5.7 ± 1.2 3.3 ± 2.5 7 3.5 ± 1.7 2.4 ± 1.0 4.1 ± 1.3 5.3 ± 3.7 3.2 ± 2.4 8 3.8 ± 1.9 3.5 ± 1.1 4.1 ± 2.0 4.6 ± 3.1 3.3 ± 2.2 9 3.1 ± 0.8 5.4 ± 3.5 3.0 ± 0.7 2.9 ± 1.8 3.0 ± 1.5 10 2.9 ± 0.5 5.1 ± 3.9 3.2 ± 1.1 2.4 ± 0.7 2.6 ± 1.3 11 2.8 ± 0.8 2.7 ± 1.4 4.5 ± 2.1 5.2 ± 3.0 3.1 ± 1.5 12 3.1 ± 1.1 2.5 ± 1.4 5.3 ± 2.3 17.8 ± 16.2 4.1 ± 1.5 13 4.6 ± 2.2 7.3 ± 9.5 15.4 ± 11.0 14.9 ± 12.8 11.0 ± 11.7 14 11.8 ± 1.6 7.1 ± 6.8 14.5 ± 5.7 5.3 ± 3.9 7.9 ± 5.2 15 14.0 ± 4.5 9.2 ± 4.4 19.5 ± 11.2 5.9 ± 5.7 10.1 ± 6.2 16 9.9 ± 6.6 10.9 ± 7.3 13.8 ± 10.4 13.0 ± 7.7 13.7 ± 8.5 17 12.2 ± 4.7 9.6 ± 10.7 20.2 ± 12.3 7.4 ± 4.7 10.3 ± 3.1 18 15.3 ± 10.7 10.7 ± 9.1 25.1 ± 17.6 6.6 ± 3.8 8.6 ± 1.1 19 18.9 ± 21.5 15.2 ± 12.4 32.3 ± 16.1 9.7 ± 6.2 12.6 ± 9.9

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Table S5. The pH and lactic acid content of digesta from the last section of piglet small intestines obtained 6 h after ingestion of acidified milk (control) or acidified milk plus Lactobacillus casei 32G. Content (mg/g sample)* Sample pH* D-lactic acid L-lactic acid Control 6.98 ± 0.27a 0.12 ± 0.03a 1.09 ± 0.25b 32G 7.28 ± 0.59a 0.10 ± 0.06a 1.51 ± 0.57a *Values in the same column with different letters differ statistically (p < 0.10).

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Table S6. Automated Ribosomal Intergenic Spacer Analysis (ARISA) profile of each piglet. Relative quantity of 16S-23S rRNA spacer amplicons (%) Size Control piglets Piglets fed with L. casei 32G No. 1 No. 2 No. 3 No. 1 No. 2 No. 3 387 34 30 18 0 0 0 391 18 15 44 0 0 0 463 0 2 0 0 0 0 481 4 2 0 5 6 0 485 13 18 6 0 0 0 489 3 3 16 0 5 3 497 7 10 0 0 1 0 502 0 0 0 42 40 28 543 2 0 0 0 0 0 559 0 0 0 0 2 0 567 5 0 0 0 0 0 573 3 2 7 0 0 0 578 0 0 0 2 0 0 614 0 0 0 2 0 2 636 0 0 0 13 2 2 678 0 0 0 0 0 6 680 0 0 0 0 3 6 688 5 7 0 0 2 5 699 0 0 0 0 1 4 706 0 0 6 0 2 0 710 0 0 0 24 22 10 720 0 0 0 2 0 0 722 0 0 0 5 6 17 728 4 6 2 5 2 0 863 0 0 0 0 2 7 870 5 5 0 0 3 9

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Table S7. Bacterial genera detected in either the piglet ileum digesta and/or tissue samples of piglets fed either acidified milk (control) or acidified milk plus Lactobacillus casei 32G. Percentage (mean ± SE) Genus Digestaa Tissueb Control 32G p-value Control 32G p-value Achromobacter 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.55 ± 0.39 0.043 Acidovorax 0.11 ± 0.04 0.05 ± 0.03 0.060 2.74 ± 1.09 0.82 ± 0.34 0.021 Acinetobacter 0.89 ± 0.14 0.54 ± 0.07 0.003 49.43 ± 18.65 19.59 ± 11.47 0.055 Actinobacillus 0.00 ± 0.00 15.83 ± 6.75 0.005 0.00 ± 0.00 0.00 ± 0.00 N/A Actinomyces 0.22 ± 0.12 0.00 ± 0.00 0.019 0.00 ± 0.00 0.00 ± 0.00 N/A Adhaeribacter 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.05 ± 0.05 0.100 Aeromonas 0.00 ± 0.00 0.02 ± 0.02 0.160 0.05 ± 0.05 16.15 ± 12.25 0.041 Agrobacterium 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.05 ± 0.05 0.100 Alistipes 0.03 ± 0.03 0.00 ± 0.00 0.089 1.42 ± 0.74 0.00 ± 0.00 0.008 Alkalibacterium 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.33 ± 0.33 0.140 Anaerotruncus 0.00 ± 0.00 0.02 ± 0.02 0.110 0.00 ± 0.00 0.00 ± 0.00 N/A Aquabacterium 0.00 ± 0.00 0.00 ± 0.00 N/A 0.55 ± 0.14 0.33 ± 0.19 0.130 Aquitalea 0.00 ± 0.00 0.03 ± 0.03 0.130 0.00 ± 0.00 0.00 ± 0.00 N/A Arcanobacterium 0.02 ± 0.02 0.00 ± 0.00 0.120 0.00 ± 0.00 0.00 ± 0.00 N/A Arcicella 0.00 ± 0.00 0.02 ± 0.02 0.170 0.00 ± 0.00 0.00 ± 0.00 N/A Arcobacter 0.02 ± 0.02 0.05 ± 0.03 0.120 0.11 ± 0.11 0.00 ± 0.00 0.110 Arthrobacter 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.05 ± 0.05 0.100 Azospira 0.00 ± 0.00 0.00 ± 0.00 N/A 0.05 ± 0.05 0.00 ± 0.00 0.110 Bacillus c 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.05 ± 0.05 0.100 Bacteroides 0.06 ± 0.03 0.00 ± 0.00 0.011 2.08 ± 1.84 0.49 ± 0.49 0.150 Bosea 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.05 ± 0.05 0.100 Brevundimonas 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 1.86 ± 0.97 0.012 Burkholderia 0.00 ± 0.00 0.00 ± 0.00 N/A 0.27 ± 0.27 0.71 ± 0.71 0.220 Campylobacter 0.00 ± 0.00 0.00 ± 0.00 N/A 0.05 ± 0.05 0.00 ± 0.00 0.110 Catenibacterium 0.00 ± 0.00 0.02 ± 0.02 0.160 0.00 ± 0.00 0.00 ± 0.00 N/A Chryseobacterium 0.00 ± 0.00 0.00 ± 0.00 N/A 0.05 ± 0.05 0.27 ± 0.20 0.098 Chryseomonas 0.00 ± 0.00 0.00 ± 0.00 N/A 0.22 ± 0.11 0.16 ± 0.09 0.320 Citrobacter 0.16 ± 0.02 0.12 ± 0.04 0.190 0.71 ± 0.63 0.05 ± 0.05 0.100 Cloacibacterium 0.00 ± 0.00 0.00 ± 0.00 N/A 0.77 ± 0.30 0.49 ± 0.49 0.280 Clostridium 22.96 ± 7.30 0.08 ± 0.04 0.001 0.11 ± 0.05 0.00 ± 0.00 0.016 Comamonas 0.05 ± 0.03 0.02 ± 0.02 0.092 5.04 ± 2.29 3.56 ± 1.66 0.250 Coprococcus 0.02 ± 0.02 0.00 ± 0.00 0.130 0.00 ± 0.00 0.00 ± 0.00 N/A Corynebacterium 0.14 ± 0.07 0.00 ± 0.00 0.005 0.00 ± 0.00 0.00 ± 0.00 N/A Cupriavidus 0.03 ± 0.03 0.00 ± 0.00 0.073 0.99 ± 0.49 0.71 ± 0.56 0.340 Dechloromonas 0.00 ± 0.00 0.00 ± 0.00 N/A 0.11 ± 0.11 0.11 ± 0.11 0.560 Delftia 0.00 ± 0.00 0.00 ± 0.00 N/A 0.22 ± 0.05 0.00 ± 0.00 0.001 Desulfovibrio 0.00 ± 0.00 0.02 ± 0.02 0.160 0.00 ± 0.00 0.00 ± 0.00 N/A Devosia 0.02 ± 0.02 0.00 ± 0.00 0.120 0.00 ± 0.00 0.00 ± 0.00 N/A Diaphorobacter 0.00 ± 0.00 0.00 ± 0.00 N/A 0.05 ± 0.05 0.05 ± 0.05 0.550 Dorea 0.02 ± 0.02 0.00 ± 0.00 0.130 0.38 ± 0.38 0.00 ± 0.00 0.120 Duganella 0.03 ± 0.03 0.00 ± 0.00 0.074 0.00 ± 0.00 0.00 ± 0.00 N/A Elizabethkingia 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.22 ± 0.14 0.031 Empedobacter 0.00 ± 0.00 0.00 ± 0.00 N/A 0.05 ± 0.05 0.05 ± 0.05 0.550 Enhydrobacter 0.05 ± 0.00 0.02 ± 0.02 0.009 0.33 ± 0.25 0.05 ± 0.05 0.110 Enterobacter 0.00 ± 0.00 0.00 ± 0.00 N/A 0.11 ± 0.05 0.05 ± 0.05 0.200 Enterococcus 0.14 ± 0.08 0.19 ± 0.03 0.260 0.33 ± 0.25 0.00 ± 0.00 0.055

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Erysipelotrichaceae 0.00 ± 0.00 0.00 ± 0.00 N/A 0.05 ± 0.05 0.00 ± 0.00 0.097 Incertae Sedis Escherichia 0.28 ± 0.21 2.67 ± 2.21 0.068 0.00 ± 0.00 0.05 ± 0.05 0.100 Exiguobacterium 0.00 ± 0.00 0.00 ± 0.00 N/A 0.22 ± 0.11 26.87 ± 26.87 0.150 Faecalibacterium 0.02 ± 0.02 0.00 ± 0.00 0.120 0.05 ± 0.05 0.00 ± 0.00 0.110 Flavimonas 0.03 ± 0.02 0.00 ± 0.00 0.005 0.05 ± 0.05 1.04 ± 0.81 0.061 Flavobacterium 0.00 ± 0.00 0.00 ± 0.00 N/A 0.16 ± 0.16 0.93 ± 0.57 0.054 Fusobacterium 0.03 ± 0.03 0.12 ± 0.07 0.064 0.00 ± 0.00 0.05 ± 0.05 0.130 Gemella 0.25 ± 0.09 0.03 ± 0.02 0.003 0.00 ± 0.00 0.00 ± 0.00 N/A Haemophilus 0.00 ± 0.00 0.30 ± 0.23 0.068 0.00 ± 0.00 0.00 ± 0.00 N/A Helicobacter 0.00 ± 0.00 0.11 ± 0.06 0.009 1.64 ± 1.24 0.11 ± 0.05 0.071 Kluyvera 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.05 ± 0.05 0.100 Lachnospiraceae 0.08 ± 0.04 0.00 ± 0.00 0.007 0.33 ± 0.33 0.22 ± 0.14 0.340 Incertae Sedis Lactobacillus 15.86 ± 5.70 38.28 ± 14.00 0.027 0.55 ± 0.29 0.55 ± 0.47 0.520 Lactococcus 0.44 ± 0.15 0.64 ± 0.12 0.110 4.60 ± 4.27 0.60 ± 0.45 0.120 Leptotrichia 0.00 ± 0.00 0.02 ± 0.02 0.160 0.00 ± 0.00 0.00 ± 0.00 N/A Leuconostoc 1.35 ± 0.23 1.18 ± 0.53 0.350 7.22 ± 6.90 0.99 ± 0.83 0.130 Marinilabilia 0.00 ± 0.00 0.00 ± 0.00 N/A 0.16 ± 0.16 0.00 ± 0.00 0.130 Methylobacterium 0.00 ± 0.00 0.06 ± 0.06 0.150 0.22 ± 0.05 0.11 ± 0.11 0.150 Microbacterium 0.02 ± 0.02 0.00 ± 0.00 0.120 0.05 ± 0.05 0.05 ± 0.05 0.560 Microvirgula 0.03 ± 0.02 0.00 ± 0.00 0.004 0.16 ± 0.16 0.00 ± 0.00 0.130 Mogibacterium 0.00 ± 0.00 0.02 ± 0.02 0.170 0.00 ± 0.00 0.00 ± 0.00 N/A Mucispirillum 0.00 ± 0.00 0.00 ± 0.00 N/A 0.05 ± 0.05 0.00 ± 0.00 0.097 Mycobacterium 0.00 ± 0.00 0.00 ± 0.00 N/A 0.05 ± 0.05 0.00 ± 0.00 0.099 Novosphingobium 0.03 ± 0.02 0.00 ± 0.00 0.005 1.31 ± 0.62 0.66 ± 0.49 0.170 Ochrobactrum 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.16 ± 0.09 0.026 Olsenella 0.02 ± 0.02 0.00 ± 0.00 0.120 0.00 ± 0.00 0.00 ± 0.00 N/A Paenibacillus 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.05 ± 0.05 0.100 Pandoraea 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.05 ± 0.55 0.100 Parabacteroides 0.00 ± 0.00 0.00 ± 0.00 N/A 0.99 ± 0.99 0.11 ± 0.11 0.140 Pasteurella 0.00 ± 0.00 0.72 ±0.29 0.001 0.00 ± 0.00 0.00 ± 0.00 N/A Pasteuriaceae 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 1.48 ± 0.85 0.039 Incertae Sedis Pedobacter 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.22 ± 0.11 0.017 Peptostreptococcaceae 21.25 ± 3.22 31.48 ± 15.11 0.230 0.16 ± 0.09 0.05 ± 0.05 0.100 Incertae Sedis Phenylobacterium 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.16 ± 0.09 0.026 Phocoenobacter 0.00 ± 0.00 0.02 ± 0.02 0.130 0.00 ± 0.00 0.00 ± 0.00 N/A Planomicrobium 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.05 ± 0.05 0.100 Prevotella 0.02 ± 0.02 0.34 ± 0.34 0.130 0.05 ± 0.05 0.00 ± 0.00 0.110 Propionivibrio 0.05 ± 0.05 0.00 ± 0.00 0.099 0.00 ± 0.00 0.00 ± 0.00 N/A Providencia 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.16 ± 0.16 0.140 Pseudomonas 0.05 ± 0.03 0.05 ± 0.05 0.990 5.47 ± 2.60 3.78 ± 1.53 0.250 Pseudoxanthomonas 0.00 ± 0.00 0.00 ± 0.00 N/A 0.05 ± 0.05 0.00 ± 0.00 0.097 Raoultella 0.02 ± 0.02 0.00 ± 0.00 0.120 0.05 ± 0.05 0.00 ± 0.00 0.097 Rhodoferax 0.00 ± 0.00 0.00 ± 0.00 N/A 0.05 ± 0.05 0.00 ± 0.00 0.097 Rickettsiella 0.02 ± 0.02 0.00 ± 0.00 0.120 0.00 ± 0.00 0.00 ± 0.00 N/A Roseburia 0.00 ± 0.00 0.00 ± 0.00 N/A 0.05 ± 0.05 0.00 ± 0.00 0.097 Rothia 0.20 ± 0.16 0.08 ± 0.04 0.160 0.00 ± 0.00 0.00 ± 0.00 N/A Ruminococcus 0.02 ± 0.02 0.00 ± 0.00 0.120 0.05 ± 0.05 0.00 ± 0.00 0.097

95

Sarcina 0.00 ± 0.00 0.00 ± 0.00 N/A 0.05 ± 0.05 0.00 ± 0.00 0.099 Schlegelella 0.00 ± 0.00 0.00 ± 0.00 N/A 0.11 ± 0.11 0.00 ± 0.00 0.110 Serratia 0.02 ± 0.02 0.02 ± 0.02 0.950 0.00 ± 0.00 0.05 ± 0.05 0.100 Shewanella 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.05 ± 0.05 0.100 Shinella 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.05 ± 0.05 0.100 Sphingobacterium 0.00 ± 0.00 0.00 ± 0.00 N/A 0.05 ± 0.05 10.18 ± 7.22 0.043 Sphingobium 0.00 ± 0.00 0.05 ± 0.03 0.044 0.16 ± 0.16 0.27 ± 0.27 0.340 Sphingomonas 0.03 ± 0.03 0.00 ± 0.00 0.089 0.22 ± 0.22 0.05 ± 0.05 0.180 Staphylococcus 0.02 ± 0.02 0.00 ± 0.00 0.120 0.00 ± 0.00 0.11 ± 0.05 0.017 Stenotrophomonas 0.02 ± 0.02 0.00 ± 0.00 0.130 0.60 ± 0.52 0.49 ± 0.28 0.430 Streptococcus 0.65 ± 0.12 0.95 ± 0.42 0.230 0.60 ± 0.60 0.11 ± 0.05 0.140 Streptophyta 0.05 ± 0.05 0.02 ± 0.02 0.200 0.22 ± 0.05 0.27 ± 0.27 0.430 Subdoligranulum 0.05 ± 0.03 0.08 ± 0.02 0.140 0.00 ± 0.00 0.00 ± 0.00 N/A Sulfurospirillum 0.00 ± 0.00 0.02 ± 0.02 0.170 0.00 ± 0.00 0.00 ± 0.00 N/A Sutterella 0.02 ± 0.02 0.00 ± 0.00 0.130 0.00 ± 0.00 0.00 ± 0.00 N/A Tannerella 0.02 ± 0.02 0.00 ± 0.00 0.130 0.00 ± 0.00 0.00 ± 0.00 N/A Tetrathiobacter 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.05 ± 0.05 0.100 TM7 genera Incertae 0.00 ± 0.00 0.02 ± 0.02 0.170 0.22 ± 0.22 1.31 ± 0.91 0.074 Sedis Turicibacter 30.05 ± 2.74 0.08 ± 0.02 0.001 0.60 ± 0.60 1.26 ± 1.18 0.270 Veillonella 3.16 ± 1.29 4.70 ± 2.30 0.240 0.38 ± 0.38 0.00 ± 0.00 0.120 Vitreoscilla 0.02 ± 0.02 0.02 ± 0.02 0.920 0.00 ± 0.00 0.00 ± 0.00 N/A Vogesella 0.00 ± 0.00 0.00 ± 0.00 N/A 0.05 ± 0.05 0.11 ± 0.11 0.300 Weissella 0.95 ± 0.21 0.98 ± 0.18 0.450 6.62 ± 6.29 0.05 ± 0.05 0.072 Zoogloea 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.05 ± 0.05 0.100 aThe detection limit was 0.0005 and this value was used to calculate the p-value. bThe detection limit was 0.002 and this value was used to calculate the p-value.

96

CHAPTER 4

Transit of Lactobacillus casei 32G through the piglet ileum and its effect on the

composition of the ileum microbiota

Kanokwan Tandee,1 Thomas Crenshaw,2 Benjamin Darien,3 Jeff Broadbent,4 and James

Steele1

Department of Food Science1; Department of Animal Science2; School of Veterinary

Medicine, University of Wisconsin-Madison3; and Department of Nutrition, Dietetics, and

Food Sciences, Utah State University4 97

Abstract

Lactobacillus casei is a widely consumed probiotic species whose primary purported health benefits are related to immune health. The mechanism(s) by which strains of L. casei modulate the host immune system are unknown; however, a restructuring of the ileum commensal microbiota may contribute to this ability. In a previous study, we demonstrated that administration of L. casei 32G at 1010 CFU/day for seven days to piglets consuming a humanized diet resulted in a significant restructuring of the ileum digesta and ileum tissue adherent microbiotas at 6 h after the last dose. The objective of the current study was to determine if these changes in the ileum microbiotas were periodic in nature. In this study, L. casei 32G was administered at a dose of 109 CFU daily for seven days; this treatment resulted in significant, relatively short-lived alterations in the composition of both the digesta and Peyer’s patch microbiotas, with these microbiotas returning to compositions that were not distinguishable from the control microbiotas within 24 h. Of particular interest in the current trial was the increases in numbers of Escherichia and Lactobacillus that occurred at the 6 and 12 h time points after the consumption of L. casei 32G; these increases occurred in both the digesta and Peyer’s patch samples. Overall, these results support the hypothesis that consumption of 32G alters the composition of the ileum microbiota and hence has potential to serve as a probiotic with immunomodulatory activity. 98

Introduction

Probiotics are live microorganisms which, when administered in adequate amounts, confer a health benefit on the host (16). A wide array of human health benefits have been attributed to the consumption of probiotics (9); the majority of which can be divided into either gastrointestinal (GI) or immune health benefits. Human GI health benefits ascribed to probiotics include reduced incidence of GI infections, improved lactose digestion, regulation of bowel transit time, and reduction in incidence of colon cancer (12, 21, 23, 31, 36).

Immune health benefits ascribed to probiotics include reduction in the incidence of inflammatory bowel diseases, enhanced immune responses, reduction in allergic reactions, and anti-inflammatory activity (18, 27, 28, 37). The most common genera of probiotics are

Lactobacillus and Bifidobacterium (3). There is significant interest in a number of species within these genera; however, Lactobacillus casei, due to its high level of consumption (> 31 million doses per day and 1020 live cells per year) is a probiotic species of particular interest

(15).

L. casei is a Gram-positive, nutritionally fastidious, aciduric, facultative anaerobic, strictly fermentative rod that can be found in various environmental habitats, including raw and fermented dairy products (especially cheese) and plant materials (e.g., wine, pickle, silage, and kimchi), as well as the oral cavity, reproductive and GI tracts of humans and animals (25). The species is well characterized on the genetic level. Multilocus sequence typing has revealed the divergence of three major lineages of L. casei approximately 1.5 million years ago (6). Additionally, a detailed analysis of the L. casei ATCC 334 genome 99 has been published (7). Finally, comparative analysis of 17 L. casei genomes has revealed an average genome content of 2,780 genes and that the species has a predicted core genome of

1,715 genes, indicating that approximately 38% of the genes are variable in an average L. casei genome (5). These results demonstrate that significant strain-to-strain variation exists within this species and that significant strain-to-strain differences in probiotic efficacy and effects are likely. Strain-to-strain variations in the ability of four L. casei strains to survive

GI passage to the ileum and adhere to the ileal epithelial surface were reported in Chapter 3.

The primary probiotic properties ascribed to strains of L. casei are related to immune health benefits. Studies with human peripheral blood mononuclear cells have demonstrated that strains of L. casei are able to modulate cytokine expression and natural killer activity

(13, 30). Additionally, human clinical studies have demonstrated that consumption of L. casei DN-114001 by elderly people increased the specific antibody responses to influenza vaccination (4) and decreased the duration of common infectious diseases, such as upper respiratory tract infections and rhinopharyngitis (22). While a number of studies have demonstrated the ability of L. casei to modulate the immune system, a detailed understanding of the mechanisms involved remains unknown. However, possible routes of immunomodulation include direct interaction of the probiotic with cells of the host immune system (i.e., via cell surface proteins or exopolysaccharides) or through the production of small molecules (i.e., via amino acid catabolites or quorum sensing molecules). These probiotic-immune system interactions may either directly result in immunomodulation (i.e., induction of cytokines) or may stimulate the production of components of the innate immune system (i.e., defensins or secretory IgA), thereby restructuring the commensal ileum 100 microbiota; the restructured commensal ileum microbiota may then modulate the host immune system by the mechanisms described above.

In a previous study (Chapter 3), we demonstrated that administration of L. casei 32G at 1010 CFU/day for seven days to piglets consuming a humanized diet resulted in a significant restructuring of the ileum digesta (i.e., reductions in Clostridium and Turicibacter as well as increases in Lactobacillus and Actinobacillus) and ileum tissue adherent (i.e., increases in Aeromonas and Sphingobacterium) microbiotas 6 h after the last dose. The objective of the current study was to determine if these changes in the ileum microbiotas were periodic in nature. In addition, this study involved piglets with a different genetic background and housed in a different environment, relative to the previous study, to assess how robust any observed changes are in the piglet ileum microbiota. The differences in piglet genotype are of particular importance given the study of Benson et al. (2) demonstrating that host genetics plays a key role in shaping the GI tract microbiota. 101

Materials and Methods

Bacterial strain. A streptomycin-rifampicin resistant (StrRRifR) L. casei 32G derivative described in a previous study was maintained at -80°C in MRS broth (BD Difco,

Sparks, MD) containing 25% (v/v) glycerol (Sigma-Aldrich, St. Louis, MO). Working cultures were prepared from frozen stocks by two sequential transfers in MRS broth and incubations were conducted statically at 37°C for 24 h and 18 h, respectively. For the second transfer, MRS broth was supplemented with 600 µg/ml streptomycin (Str; Sigma-Aldrich) and 100 µg/ml rifampicin (Rif; Sigma-Aldrich). The culture was harvested by centrifugation at 5,000 ×g for 10 min at 25°C. The pellet was resuspended in 0.85% NaCl (w/v) and the optical density at 600 nm (OD600) determined. A volume of washed cells (based upon the

OD600) sufficient to yield a 1.4 L cell suspension with an OD600 of 6.0 was harvested by centrifugation at 5,000 ×g and washed with 1.4 L of 0.85% NaCl. The resulting pellet was suspended in 140 ml of skim milk (Babcock Hall Dairy Store, Madison, WI) to obtain a final concentration of 109 CFU/ml. The pH of skim milk containing L. casei 32G was adjusted to

4.2 by the addition of 50% L-lactic acid (Sigma-Aldrich). The acidified milk culture was divided into 4 ml aliquots, and stored at 4°C until fed (less than 8 days). The culture was enumerated daily on MRS supplemented with streptomycin and rifampicin (MRS Str Rif) agar prior to being fed to the piglets.

Animals. Twenty-four crossbred piglets (1/2 PIC Line 15 x 1/4 Landrace x 1/4 Large

White) weaned at the age of 21-22 days old were obtained from the UW Swine Research and

Teaching Center, Arlington, WI. They were housed in six groups with four piglets in each 102 group at the UW-Madison Livestock Laboratory with solid partition barriers between groups.

The housing temperature was maintained at 26.7°C and continuous access to water was allowed. Piglets were fed the humanized diet described in Chapter 3 at 7 AM, 12 PM, and 6

PM for 12 days. Animal pens and feed troughs were cleaned and sanitized between each meal. Five groups were fed daily 4 ml of an acidified milk culture containing 109 CFU of L. casei 32G for seven days, while a control group was fed 4 ml of acidified milk. Piglet weight was determined at four-day intervals and used to calculate the amount of food offered on subsequent days. Piglets were offered an amount of food for each meal that would be consumed within a 20-min feeding interval.

Sample collection. Piglets were sacrificed at 1.5, 6, 12, 24, and 72 h after the last dose by electrical stunning through brain followed by exsanguination. A 15 cm-long ileum section was dissected at 10 cm above the ileocecal junction and the digesta was recovered by manual distension and serially diluted to 10-5 in 0.85% NaCl. A 4 cm2 section of the Peyer’s patch from an ileum section was removed, washed twice in 10 ml of 0.85% NaCl, homogenized using a PT 10/35 homogenizer (Brinkmann Instruments, Delran, NJ), and diluted to 10-5 in 0.85% NaCl. The 10-1 and 10-2 dilutions were plated using the standard pour-plated method, while 10-2 to 10-5 dilutions were plated using the drop-plated method

(26). MRS Str Rif agar plates supplemented with 50 U/ml nystatin, to inhibit the fungal growth (Sigma-Aldrich), were utilized to select for the L. casei 32G. Agar plates were incubated at 37°C for 48 h prior to enumeration. The 10-1 dilution of digesta and Peyer’s patch samples was maintained at -20°C for further analysis. To confirm the identity of cultures recovered, 24 colonies were randomly selected from the MRS Str Rif agar plates 103 collected from each group. These cultures were examined for catalase activity (17); the catalase-negative cultures were compared to the fed strain by pulsed field gel electrophoresis

(PFGE) as described previously (6).

16S rRNA pyrosequencing. Total DNA from 0.2 ml of 10-1 dilutions of digesta and

Peyer’s patch samples was isolated using the QIAamp DNA Stool Mini Kit (Qiagen

Sciences, MD). Partial 16S rRNA sequences were determined by Roche-454 GS FLX

Titanium technology at University of Nebraska-Lincoln, Core for Applied Genomics and

Ecology (CAGE) as previously described (2). Briefly, the V1-V2 region was amplified using bar-coded fusion primers with the Roche-454 A or B titanium sequencing adapters (shown in italics), followed by a unique 8-base barcode sequence (B) and finally the 3' ends of primer

A-8FM (5' - CCA TCT CAT CCC TGC GTG TCT CCG ACT CAG BBB BBB BBA GAG

TTT GAT CMT GGC TCA G - 3') and of primer B-357R (5' - CCT ATC CCC TGT GTG

CCT TGG CAG TCT CAG BBB BBB BBC TGC TGC CTY CCG TA - 3'). All PCR reactions were quality-controlled for amplicon saturation by gel electrophoresis; band intensity was quantified against standards using GeneTools software (Syngene). The amplicons from all reactions were pooled in equal amounts and gel purified. The resulting products were quantified using PicoGreen (Invitrogen) and a Qubit fluorometer (Invitrogen) before sequencing. The data processing pipeline removed low-quality reads that: 1) did not completely match the PCR primer and barcode; 2) were shorter than 200 bp or longer than

500 bp in length; 3) contained more than two undetermined nucleotides (N); and 4) had an average quality score over 20. After filtering, each read was trimmed to remove 5' adapter and primer sequences and was parsed by a barcode. The taxonomic status was assigned to 104 each read using a parallelized version CLASSIFIER (40). At the standard threshold of 0.8, reads were classified down to the lowest level until the score < 0.8, at which point reads were classified as “unclassified” at the next-higher taxonomic rank. The output data from

CLASSIFIER, which were the numbers of reads in each genus, were normalized across the same type of samples using rarefaction in QIIME (8).

Statistical analysis. L. casei 32G numbers were presented as log CFU/g and log

CFU/cm2 with the standard error of mean (SE) for the ileum digesta and Peyer’s patch samples, respectively. For pyrosequencing, normalized data were presented as mean ± SE.

The statistical difference between treatments was tested by between group analysis in package ade4 (14) of R 2.14.0 (32) as described by de Carcer et al (11). The dominant genera that were promoted or inhibited were determined by correspondence analysis in package ade4 of R 2.14.0 as described by de Carcer et al (11). 105

Results and discussion

Transit of L. casei 32G through the piglet ileum. To investigate the population dynamics of L. casei 32G in piglet ileum digesta and Peyer’s patches when 32G was fed at

109 CFU/day for seven days, MRS Str Rif plates were utilized to enumerate 32G over a 72 h period after the last dose was administered. These results are presented in Figure 1. L. casei

32G was not recovered from the control piglets, while endogenous Lactobacillus spp. were present at the levels of 106-108 CFU/g and 104-106 CFU/cm2 in the ileum digesta and Peyer’s patch samples of the control piglets, respectively (data not shown). PFGE analysis of 120 colonies isolated from MRS Str Rif plates indicated that 67% and 73% of the isolates from piglet ileum digesta and Peyer’s patch samples, respectively, were indistinguishable from

32G (data not shown). The levels of 32G in both the digesta and Peyer’s patch samples were lower than that observed in our previous study (Chapter 3), likely reflecting the 10-fold lower dose utilized in this study. L. casei 32G was present at the highest levels in both the digesta and Peyer’s patches during the first 6 h after consumption of the last dose and was undetectable after 24 h (Figure 1). These results are consistent with our previous study and indicate that the acidified milk containing L. casei reached the piglet ileum within approximately 6 h of consumption (Chapter 3). The ability of probiotic strains to adhere to the piglet ileum surface has been reported previously (33, 39) and this ability is thought to be related to immunomodulatory ability of probiotics, as host-microbe interactions are primarily found in the ileum. The levels of L. casei 32G decreased to below the limit of detection (10

CFU) in both sample types by 24 h, indicating that L. casei 32G only transiently colonizes 106 the piglet ileum. The periodic nature of the presence of L. casei 32G in the ileum, and likely probiotics taken daily in general, may be of significance in immune modulation (1, 10, 34) as the host immune system is intolerance to probiotics.

Overall microbiota composition. The mammalian GIT is a series of anatomical regions with distinct microbiotas (35) and the GIT microbiota is known to have significant influence on health (20). The GIT microbiota that has received the greatest attention has been the microbiota of the colon, with most studies utilizing feces as a surrogate (19). The small intestine microbiota has received considerably less attention, in part due to the difficulties associated with obtaining samples. However, the small intestine microbiota has a significant influence on health, as it is the primary site of interaction between the GIT microbiota and the gut associated lymphoid tissue (GALT) (29). Peyer’s patches, an important component of GALT, are aggregated lymphoid tissue present in the small intestine and are thought of as the immune sensors of the GIT (24). To assess the influence of administration of L. casei 32G on the microbiota of the piglet ileum digesta and Peyer’s patches, 16S rRNA pyrosequencing was used to characterize these microbiotas in the control and L. casei 32G-fed piglets. A total of 609,464 filtered reads were obtained from the 48 ileum digesta and Peyer’s patch samples; the numbers of reads varied from 2,113 to 45,966 with an average of 18,057 reads per sample for the ileum digesta samples and from 2,302 to

15,290 with an average of 7,656 reads per sample for the Peyer’s patch samples. After the taxonomic status of each read was assigned by CLASSIFIER, there were 16 phyla, 21 classes, 47 orders, 108 families, and 290 genera identified overall. Between group analysis indicated that the ileum digesta microbiota samples were significantly different (p < 0.01) 107 from the Peyer’s patch microbiota samples (Figure S1), similar to results observed in our previous L. casei 32G piglet feeding study (Chapter 3). Therefore, the ileum digesta and

Peyer’s patch microbiotas were analyzed separately.

To assess the effect of L. casei 32G administration on the ileum digesta and Peyer’s patch microbiotas between group analysis was conducted. The ileum digesta microbiotas of the 32G-fed piglets at different time points (1.5, 6, 12, 24, and 72 h) were examined relative to the control piglets and at 1.5, 6, and 12 h they were determined to be significantly (p <

0.05) different (Figure 2). The same analysis was utilized to evaluate the effect of L. casei

32G on the Peyer’s patch adherent microbiota at different time points; only the 12 h time point differed significantly (p < 0.05) from the control Peyer’s patch microbiota (Figure 3).

These results are similar to those observed in our previous study (Chapter 3), which was conducted with a 10-fold higher dose, in that a significant difference was observed in the overall microbiota composition digesta microbiota at the 6 h time point, while no significant difference was observed in ileum tissue samples at 6 h. This study demonstrates that daily administration of L. casei 32G resulted in significant alterations in both the ileum digesta and

Peyer’s patch adherent microbiotas and that these alterations are periodic in nature. Daily consumption of 32G resulted in significant, relatively short-lived alterations to the composition of both the digesta and Peyer’s patch microbiotas, with these microbiotas returning to compositions that were not distinguishable from the control microbiota within 24 h.

Composition of ileum digesta microbiota. To assess temporal changes in the composition of the ileum digesta microbiota resulting from the administration of L. casei 108

32G, correspondence analysis was conducted with the piglet 16S rRNA pyrosequencing data.

Temporal changes to the ileum digesta microbiota are apparent at the phylum level (Table 1 and Figure 4a). Consumption of 32G resulted in a non-significant (p > 0.09) reduction in

Bacteriodetes and a significant (p < 0.05) increase in Proteobacteria at 1.5, 6, and 12 h.

Although reductions in the level of Firmicutes were observed at 6 and 12 h post 32G consumption, these changes were not statistically significant (p > 0.07). The observed changes at the phylum level are similar to those observed in our previous 32G piglet trial

(Chapter 3), in which 32G consumption resulted in a reduction in the level of Firmicutes

(96.60 vs. 62.85%; p < 0.0001) and an increase in the level of Proteobacteria (2.45 vs.

36.54%; p < 0.001) relative to the control piglets 6 h after 32G consumption. A significant difference from our previous trial is the abundance of Bacteroidetes in the current study

(17.02% of the control ileum digesta microbiota), while this phylum only comprised 0.27% of the microbiota in the previous trial; differences in the ileum digesta microbiotas in the control piglets between these studies are likely due to differences in piglet genetics, the dose of 32G administered, and the housing environment. These results confirm our previous study and demonstrate that consumption of L. casei 32G results in a significant restructuring the of piglet ileum digesta microbiota, even when examined at the phylum level.

To identify differences in the ileum digesta microbiota resulting from the administration of 32G at the genus level, correspondence analysis was conducted. The results of this analysis indicate that 19, 18, 24, 12, and 11 genera of the ileum digesta microbiota were significantly (p < 0.05) different than those present in the control at 1.5, 6,

12, 24, and 72 h after administration of L. casei 32G, respectively (Table 1). These results 109 are consistent with the between group analysis and indicate that administration of 32G results in a significant alteration in the ileum digesta microbiota and that the microbiota returns to its pre-disturbed state over a period of days. The genus level alterations are presented in Table 1 and Figure 4b. The numerically largest change was observed in the prevalence of

Escherichia which comprised 3.18% of the control piglets ileum digesta and significantly (p

< 0.05) increased in prevalence to comprise to 7.97, 33.81, 38.70, and 26.87% of the ileum digesta microbiota at 1.5, 6, 12, and 24 h after 32G consumption, respectively; the prevalence of Escherichia subsequently declined to 3.42% at 72 h, which was not significantly (p =

0.46) different then its prevalence in the control piglets. An increase was also observed in the prevalence of Lactobacillus upon administration of L. casei 32G from 25.36% in the control piglet ileum digesta to 45.36, 33.30, 41.56, 48.40, and 58.86% in the piglets 1.5, 6,

12, and 24 h after 32G consumption, respectively; however these percentages were not statistically different from the control piglets. Components of dominant genera (> 1% of the total microbiota) that declined in preponderance in the 32G-fed piglets at one or more time points included Clostridium, Megasphaera, Mitsuokella, Peptostreptococcaceae Incertae

Sedis, Prevotella, and Turicibacter. In our previous 32G piglet trial (Chapter 3), statistically significant reductions in the preponderance of Turicibacter (30.05 vs. 0.08%; p = 0.001) and

Clostridium (22.96 vs. 0.08%; p = 0.001) and increases in the percentages of Actinobacillus

(< 0.0005 vs. 15.83%; p = 0.005) and Lactobacillus (15.86 vs. 38.38%; p = 0.027) were observed relative to the control piglets 6 h after 32G consumption. The increase observed in

Escherichia preponderance in comparison to our previous trial (Chapter 3) may be a reflection of differences in either the piglet genotypes or the compositions of their initial 110 ileum microbiota. The consistency in declines in Clostridium preponderance in both trials supports the hypothesis that 32G may have potential for the control of Clostridium difficile infection, a serious medical condition that has been shown to be prevented by administration of a probiotic preparation containing L. casei (38).

Composition of the Peyer’s patch microbiota. To assess temporal changes in the composition of the ileum Peyer’s patch microbiota resulting from the administration of L. casei 32G, correspondence analysis was conducted with the piglet 16S rRNA pyrosequencing data. Temporal changes to the ileum Peyer’s patch microbiota are apparent at the phylum level (Table 2 and Figure 5a). Consumption of 32G resulted in a significant reduction in Bacteriodetes and a significant increase in Proteobacteria at 6 and 12 h.

Although reductions in the level of Firmicutes were observed at 6 and 12 h post 32G consumption, the change at 12 h was not statistically significant (p = 0.24). The Peyer’s patch microbiota observed in the present study, at the phylum level, was significantly different from the ileum tissue adherent microbiota observed in our previous study (Chapter

3). In the current study, the Peyer’s patch adherent microbiota from the control piglets consisted of 72.28% Firmicutes, 15.46% Proteobacteria, 9.21% Bacteriodetes, 1.34%

Fusobacteria, and 1.26% Actinobacteria. The ileum tissue adherent microbiota from the control piglets in our previous study consisted of 66.31% Proteobacteria and 22.67%

Firmicutes, with less than 1% of the microbiota consisting of Actinobacteria and

Cyanobacteria. While the consumption of 32G did not result in any statistically significant changes in these phyla, Firmicutes preponderance tended to increase and Proteobacteria tended to decrease (Chapter 3). The differences in the ileum tissue microbiotas in the control 111 piglets between these studies are likely due to differences in piglet genetics, the samples analyzed (Peyer’s patch vs. an ileum cross-sections), the dose of 32G administered, and housing environments.

To identify differences in the ileum Peyer’s patch microbiota resulting from the administration of 32G at the genus level, correspondence analysis was conducted. The results of this analysis indicate that 26, 26, 29, 23, and 29 genera of the Peyer’s patch microbiota were significantly (p < 0.05) different then the control at 1.5, 6, 12, 24, and 72 h after administration of L. casei 32G, respectively (Table 2). These results indicate that the alterations caused by 32G administration were significantly greater than indicated by the between group analysis, that administration of 32G resulted in a significant alteration in the ileum Peyer’s patch microbiota, and that not all alterations returned to their initial state

(control piglet composition) over a period of three days. The genus level alterations are presented in Table 2 and Figure 5b. The numerically largest changes were increases in prevalence of Escherichia and Lactobacillus. Escherichia that comprised 1.45% of the control piglets ileum Peyer’s patch microbiota significantly (p < 0.05) increased to 2.28,

11.46, and 13.44% of the Peyer’s patch microbiota at 1.5, 6, and 12 h after 32G consumption, respectively; the prevalence of Escherichia subsequently declined to 7.51 and 2.40% at 24 and 72 h, respectively, which were not significantly (p > 0.08) different then their prevalence in the control piglets. Lactobacillus comprised 5.33% of the control ileum Peyer’s patch microbiota and increased to 22.35, 8.47, 19.46, 10.33, and 13.69% of the Peyer’s patch microbiota at 1.5, 6, 12, 24, and 72 h after 32G consumption, respectively; other than the 6 h time point, these changes were significant (p < 0.05) relative to the control piglet Peyer’s 112 patch levels. The numerically largest decreases in prevalence were in Leuconostoc,

Prevotella, and Streptococcus. Leuconostoc that comprised 32.20% of the control piglets ileum Peyer’s patch microbiota significantly (p < 0.05) decreased to 21.65, 24.94, and

22.37% of the Peyer’s patch microbiota at 1.5, 6, and 12 h after 32G consumption, respectively; the prevalence of Leuconostoc subsequently increased to 34.63 and 27.40% at

24 and 72 h, respectively, which were not significantly (p > 0.18) different then its prevalence in the control piglets. Prevotella that comprised 7.20% of the control piglets ileum Peyer’s patch microbiota decreased to 3.03, 0.40, 2.02, 1.32, and 1.77% of the Peyer’s patch microbiota at 1.5, 6, 12, 24 and 72 h after 32G consumption, respectively; other than the 6 h time point, these changes were not significant (p < 0.05) relative to the control piglet

Peyer’s patch levels. Streptococcus which comprised 10.15% of the control piglets ileum

Peyer’s patch microbiota decreased to 6.40, 7.10, 6.00, 8.56, and 6.02% of the Peyer’s patch microbiota at 1.5, 6, 12, 24 and 72 h after 32G consumption, respectively; other than the 24 h time point, these changes were significant (p < 0.05) relative to the control piglet Peyer’s patch levels. In our previous 32G piglet trial (Chapter 3), statistically significant reduction in the preponderance of Acinetobacter (49.43 vs. 19.59%; p = 0.055) and increases in the percentages of Aeromonas (0.05 vs. 16.15%; p = 0.041) and Sphingobacterium (0.05 vs.

10.18%; p = 0.043) were observed in the ileum tissue adherent microbiota relative to the control piglets 6 h after 32G consumption. The differences in the ileum tissue microbiotas in the control piglets between these studies makes it difficult to generalize the influence of 32G on the piglet ileum tissue adherent microbiotas; however, in both studies, statistically significant alterations were observed in genera that comprise these microbiotas. 113

Conclusion

L. casei 32G when administered as a probiotic dose of 109 CFU transits through the piglet ileum within 6 to 12 h after consumption; after 24 h, it is no longer detectable in either the ileum digesta or Peyer’s patch samples. Daily consumption of 32G resulted in significant, relatively short-lived alterations to the composition of both the digesta and

Peyer’s patch microbiotas, with the digesta microbiota returning to compositions that were not distinguishable from the control microbiotas within 24 h. Of particular interest in the current trial was the increases in numbers of Escherichia and Lactobacillus that occurred at the 6 and 12 h time points after the consumption of L. casei 32G; these increases occurred in both the digesta and Peyer’s patch samples. The increase in Lactobacillus was expected as

109 CFU/day of L. casei 32G were being fed; however, the increase in Escherichia was not expected. The increases in Escherichia and Lactobacillus preponderance were accompanied by decreases in the preponderance of Clostridium, Megasphaera, Mitsuokella,

Peptostreptococcaceae Incertae Sedis, Prevotella, and Turicibacter in the ileum digesta samples; while in the Peyer’s patch adherent microbiotas, decreases were observed in

Leuconostoc, Prevotella, and Streptococcus. In our previous trial, the most significant differences between control and 32G-fed piglet ileum digesta microbiotas were reductions in the preponderance of Turicibacter (30.05 vs. 0.08%) and Clostridium (22.96 vs. 0.08%) and increases in the percentage of Lactobacillus (15.86 vs. 38.28%) and Actinobacillus (< 0.0005 vs. 15.83%); in the tissue samples, the most significant changes were increases in Aeromonas and Sphingobacterium and a decrease in Acinetobacter. It is interesting to note that in our 114 previous study Escherichia increased in the digesta samples from comprising 0.28% of the population in the control piglets to comprising 2.67% of the population in the 32G-fed piglets; however, the p-value (0.068) did not reach a level considered significant (p < 0.05) in this analysis. Overall, these results support the hypothesis that consumption of 32G alters the composition of the ileum microbiota, which could affect the host-microbe interaction, and hence has potential to serve as a probiotic with immunomodulatory activity. 115

Acknowledgements

This project was supported by Dupont Inc., the United States Department of

Agriculture, and by a Thai Government Ministry of Science and Technology scholarship for

K. Tandee. We appreciate the technical contributions from Mateo F. Budinich, Busra Aktas,

Dr. Ekkarat Phrommao, Dr. Viriya Nitteranon, JeeHwan Oh, Kurt Selle, Lulu Hoza, Elena

Vinay-Lara, Samuel Garber, and students from Department of Animal Science, University of

Wisconsin-Madison. Humanized diet was prepared in the Department of Meat Science facility, University of Wisconsin-Madison. We also thank Dr. Jaehyong Kim and Nina

Murray from the University of Nebraska and Dr. Eline Klaassens, CSIRO Livestock

Industries-Australia, for assistance with the microbiota data analysis. Peggy Steele, a member of Steele’s family, is an employee of DuPont Inc. 116

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Figure 1. Transit of L. casei 32G in the piglet ileum digesta (----) and Peyer’s patch (–––). 121

Figure 2. The microbiota composition of piglet ileum digesta at the genus level was clustered by condition and sampling time after the last dose as the result of between group analysis. The blue, red, orange, yellow, green, and purple eclipses represent the control group and L. casei 32G groups at 1.5, 6, 12, 24, and 72 h, respectively. 122

Figure 3. The microbiota composition of piglet ileum Peyer’s patch at the genus level was clustered by condition and sampling time after the last dose as the result of between group analysis. The blue, red, orange, yellow, green, and purple eclipses represent the control group and L. casei 32G groups at 1.5, 6, 12, 24, and 72 h, respectively. 123

(a)

(b)

Figure 4. The microbiota composition of piglet ileum digesta at the (a) phylum or (b) genus level in the control group and L. casei 32G groups at 1.5, 6, 12, 24, and 72 h. Only phyla or genera with over 1% of the total bacteria are presented. 124

(a)

(b)

Figure 5. The microbiota composition of piglet ileum Peyer’s patch at the (a) phylum or (b) genus level in the control group and L. casei 32G groups at 1.5, 6, 12, 24, and 72 h. Only phyla or genera with over 1% of the total bacteria are presented.

125

Table 1. Changes in the composition of piglet ileum digesta microbiota resulting from administration of L. casei 32G. Percentage (mean ± SE)a Name Control L. casei 32G 1.5 h 1.5 h 6 h 12 h 24 h 72 h Phyla Bacteroidetes 17.02 ± 15.48 1.72 ± 1.53 4.82 ± 4.34 0.32 ± 0.17 8.25 ± 6.32 6.13 ± 5.97 Firmicutes 63.35 ± 11.63 63.46 ± 8.07 46.43 ± 6.78 49.34 ± 17.72 63.92 ± 15.78 84.46 ± 7.58* Fusobacteria 0.02 ± 0.01 0.13 ± 0.04* 5.87 ± 3.41* 0.88 ± 0.83 0.21 ± 0.20 0.02 ± 0.02 Proteobacteria 19.36 ± 7.05 34.58 ± 7.18* 42.75 ± 8.87* 49.29 ± 17.46* 27.40 ± 13.30 9.13 ± 3.04 Genera Acidaminococcus 0.40 ± 0.17 0.11 ± 0.07* 0.17 ± 0.11 0.33 ± 0.29 0.05 ± 0.03* 0.06 ± 0.06* Actinobacillus 3.26 ± 1.54 6.84 ± 1.49* 2.27 ± 1.28 0.02 ± 0.02* 0.08 ± 0.05* 0.17 ± 0.17* Anaerovorax 0.10 ± 0.08 0.00 ± 0.00* 0.00 ± 0.00* 0.00 ± 0.00* 0.03 ± 0.02 0.10 ± 0.10 Aquabacterium 0.05 ± 0.03 0.02 ± 0.02 0.02 ± 0.02 0.00 ± 0.00* 0.08 ± 0.06 0.03 ± 0.02 Bacteroides 0.03 ± 0.02 0.14 ± 0.07* 0.35 ± 0.15* 0.13 ± 0.07* 0.97 ± 0.90* 0.54 ± 0.54 Blastomonas 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.03 ± 0.02* 0.00 ± 0.00 Clostridium 2.66 ± 1.00 2.65 ± 1.79 0.00 ± 0.00* 0.27 ± 0.15* 0.19 ± 0.19* 4.64 ± 4.39 Desulfovibrio 0.03 ± 0.02 0.00 ± 0.00* 0.00 ± 0.00* 0.02 ± 0.02 0.03 ± 0.03 0.05 ± 0.05 Dialister 0.54 ± 0.23 0.00 ± 0.00* 0.02 ± 0.02* 0.00 ± 0.00* 0.03 ± 0.03* 0.00 ± 0.00* Erysipelotrichaceae IS 0.05 ± 0.03 0.05 ± 0.05 0.03 ± 0.03 0.00 ± 0.00* 0.10 ± 0.05 0.16 ± 0.16 Escherichia 3.18 ± 0.84 7.97 ± 3.76* 33.81 ± 8.47* 38.70 ± 18.14* 26.87 ± 20.76* 3.42 ± 2.74 Faecalibacterium 0.63 ± 0.59 0.06 ± 0.04 0.10 ± 0.10 0.00 ± 0.00* 0.41 ± 0.37 0.14 ± 0.14 Flavobacterium 0.03 ± 0.02 0.13 ± 0.13 0.00 ± 0.00* 0.00 ± 0.00* 0.08 ± 0.08 0.10 ± 0.05 Fusobacterium 0.03 ± 0.02 0.16 ± 0.08* 6.29 ± 3.48* 1.19 ± 1.13* 0.03 ± 0.03 0.00 ± 0.00* Haemophilus 0.00 ± 0.00 0.05 ± 0.03* 0.02 ± 0.02 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 Klebsiella 0.00 ± 0.00 0.00 ± 0.00 0.05 ± 0.03* 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 Lactobacillus 25.36 ± 11.61 45.36 ± 14.76 33.30 ± 6.99 41.56 ± 13.53 48.40 ± 23.78 50.86 ± 22.30 Leuconostoc 4.99 ± 4.05 1.30 ± 0.92 2.72 ± 1.76 3.56 ± 3.19 2.66 ± 1.96 2.01 ± 1.25 Megasphaera 9.89 ± 8.04 0.03 ± 0.03* 0.27 ± 0.17* 1.31 ± 1.25 0.35 ± 0.23* 0.33 ± 0.33* Mitsuokella 2.96 ± 1.65 0.08 ± 0.05* 0.33 ± 0.20* 1.54 ± 1.49 0.25 ± 0.14* 0.19 ± 0.19*

125

126

Moraxella 0.05 ± 0.03 0.00 ± 0.00* 0.03 ± 0.03 0.02 ± 0.02 0.00 ± 0.00* 0.05 ± 0.05 Neisseria 0.22 ± 0.14 0.40 ± 0.18 0.13 ± 0.04 0.00 ± 0.00* 0.00 ± 0.00* 0.00 ± 0.00* Olsenella 0.13 ± 0.09 0.00 ± 0.00* 0.02 ± 0.02 0.00 ± 0.00* 0.02 ± 0.02 0.00 ± 0.00* Parabacteroides 0.16 ± 0.14 0.10 ± 0.10 0.30 ± 0.26 0.00 ± 0.00* 0.49 ± 0.43 0.14 ± 0.14 Peptostreptococcaceae IS 10.27 ± 4.96 0.43 ± 0.23* 0.00 ± 0.00* 0.71 ± 0.63* 0.00 ± 0.00* 5.53 ± 3.88 Prevotella 17.44 ± 16.35 0.95 ± 0.95 5.18 ± 4.81 0.05 ± 0.02* 2.55 ± 1.84 3.83 ± 3.81 Roseburia 0.67 ± 0.64 0.10 ± 0.10 0.03 ± 0.02 0.00 ± 0.00* 0.35 ± 0.27 0.35 ± 0.35 Salmonella 0.00 ± 0.00 0.02 ± 0.02 0.11 ± 0.05* 0.27 ± 0.12* 0.00 ± 0.00 0.05 ± 0.02* Sarcina 0.02 ± 0.02 20.06 ± 16.90* 0.00 ± 0.00 0.33 ± 0.24* 0.00 ± 0.00 0.22 ± 0.20 Streptococcus 0.57 ± 0.23 1.36 ± 0.35* 0.35 ± 0.19 2.96 ± 1.40* 0.74 ± 0.43 0.54 ± 0.28 Subdoligranulum 0.19 ± 0.13 0.10 ± 0.06 0.27 ± 0.27 0.00 ± 0.00* 0.49 ± 0.49 0.81 ± 0.81 Sulfurospirillum 0.05 ± 0.03 0.00 ± 0.00* 0.00 ± 0.00* 0.00 ± 0.00* 0.05 ± 0.05 0.00 ± 0.00* Sutterella 0.03 ± 0.03 0.03 ± 0.02 0.35 ± 0.17* 0.00 ± 0.00 0.14 ± 0.08 0.13 ± 0.13 Tessaracoccus 0.00 ± 0.00 0.03 ± 0.02* 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.02 ± 0.02 Turicibacter 1.31 ± 0.94 4.09 ± 2.20 0.00 ± 0.00* 0.68 ± 0.53 0.22 ± 0.22 14.02 ± 12.95 Veillonella 0.33 ± 0.14 0.44 ± 0.04 4.75 ± 2.07* 0.74 ± 0.20* 0.19 ± 0.09 0.16 ± 0.10 Vogesella 0.03 ± 0.02 0.03 ± 0.03 0.00 ± 0.00* 0.00 ± 0.00* 0.05 ± 0.05 0.00 ± 0.00* Weissella 5.96 ± 5.43 1.20 ± 0.59 3.23 ± 1.89 0.70 ± 0.63 1.51 ± 0.98 1.33 ± 1.01 Yersinia 0.02 ± 0.02 0.13 ± 0.07* 0.05 ± 0.02 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 aThe detection limit was 0.0006 and this value was used to calculate the p-value. *Genera that differ (p<0.05) from the control piglets.

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Table 2. Changes in the composition of piglet ileum Peyer’s patch microbiota resulting from administration of L. casei 32G. Name Percentage (mean ± SE) Control L. casei 32Ga 1.5 h 1.5 h 6 h 12 h 24 h 72 h Phyla Actinobacteria 1.26 ± 0.19 0.89 ± 0.32 0.92 ± 0.20 0.79 ± 0.25 1.47 ± 0.38 0.76 ± 0.04* Fusobacteria 1.34 ± 0.16 1.43 ± 0.31 2.51 ± 1.11 0.87 ± 0.12* 0.91 ± 0.06* 1.88 ± 0.48 Bacteroidetes 9.21 ± 6.34 5.73 ± 2.19 3.32 ± 1.89 3.77 ± 1.94 3.38 ± 0.44 4.56 ± 1.99 Proteobacteria 15.46 ± 2.97 19.14 ± 4.08 32.21 ± 2.98* 25.94 ± 5.10* 20.73 ± 6.48 17.56 ± 2.51 Firmicutes 72.28 ± 5.09 72.67 ± 2.15 60.91 ± 2.88* 68.50 ± 3.70 73.40 ± 6.83 75.00 ± 2.29 Genera Acidaminococcus 0.23 ± 0.20 0.06 ± 0.04 0.02 ± 0.02 0.53 ± 0.39 0.00 ± 0.00* 0.21 ± 0.21 Acinetobacter 5.50 ± 2.02 3.77 ± 1.04 5.48 ± 1.59 2.57 ± 0.89* 2.87 ± 0.55* 3.47 ± 1.20 Actinobacillus 0.23 ± 0.05 1.47 ± 1.16* 0.57 ± 0.29 0.08 ± 0.03* 0.15 ± 0.08 0.10 ± 0.04* Allobaculum 0.00 ± 0.00 0.06 ± 0.04* 0.02 ± 0.02 0.00 ± 0.00 0.00 ± 0.00 0.04 ± 0.02* Anaerobiospirillum 0.06 ± 0.04 0.00 ± 0.00* 0.00 ± 0.00* 0.00 ± 0.00* 0.00 ± 0.00* 0.00 ± 0.00* Arcobacter 0.11 ± 0.04 0.19 ± 0.10 0.11 ± 0.05 0.08 ± 0.05 0.05 ± 0.03 0.13 ± 0.02 Arthrobacter 0.00 ± 0.00 0.04 ± 0.02* 0.02 ± 0.02 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 Bacillus d 0.06 ± 0.04 0.00 ± 0.00* 0.00 ± 0.00* 0.00 ± 0.00* 0.00 ± 0.00* 0.00 ± 0.00* Bacteroides 0.30 ± 0.08 0.30 ± 0.11 0.34 ± 0.12 0.10 ± 0.04* 0.43 ± 0.07 0.13 ± 0.05* Blastomonas 0.06 ± 0.04 0.04 ± 0.02 0.04 ± 0.04 0.00 ± 0.00* 0.05 ± 0.03 0.00 ± 0.00* Citrobacter 2.15 ± 0.21 2.09 ± 0.63 3.39 ± 0.91* 1.87 ± 0.43 1.47 ± 0.35* 3.12 ± 1.31 Cloacibacterium 0.02 ± 0.02 0.06 ± 0.04 0.46 ± 0.43 0.06 ± 0.04 0.03 ± 0.03 0.02 ± 0.02 Clostridium 0.10 ± 0.06 2.21 ± 2.01* 0.08 ± 0.00 0.08 ± 0.08 0.00 ± 0.00* 0.25 ± 0.11 Corynebacterium 0.06 ± 0.02 0.04 ± 0.02 0.08 ± 0.05 0.02 ± 0.02 0.03 ± 0.03 0.00 ± 0.00* Cupriavidus 0.06 ± 0.04 0.00 ± 0.00* 0.00 ± 0.00* 0.02 ± 0.02 0.00 ± 0.00* 0.02 ± 0.02 Curvibacter 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.08 ± 0.05* 0.00 ± 0.00 0.00 ± 0.00 Desulfovibrio 0.00 ± 0.00 0.02 ± 0.02 0.06 ± 0.04* 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 Dialister 0.15 ± 0.05 0.00 ± 0.00* 0.04 ± 0.04* 0.04 ± 0.04* 0.00 ± 0.00* 0.10 ± 0.10 * Enhydrobacter 0.36 ± 0.08 0.27 ± 0.15 0.63 ± 0.20 0.48 ± 0.35 0.48 ± 0.23 0.88 ± 0.41 127

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Enterobacter 0.02 ± 0.02 0.02 ± 0.02 0.08 ± 0.03* 0.00 ± 0.00 0.03 ± 0.03 0.06 ± 0.06 Enterococcus 0.84 ± 0.14 0.44 ± 0.05* 0.70 ± 0.08 0.67 ± 0.07 0.53 ± 0.19 0.67 ± 0.10 Erysipelotrichaceae IS 0.11 ± 0.07 0.10 ± 0.05 0.06 ± 0.04 0.00 ± 0.00* 0.05 ± 0.05 0.00 ± 0.00* Escherichia 1.45 ± 0.20 2.28 ± 0.51* 11.46 ± 4.98* 13.44 ± 4.72* 7.51 ± 6.33 2.40 ± 0.94 Faecalibacterium 0.40 ± 0.26 0.13 ± 0.06 0.04 ± 0.02* 0.06 ± 0.04* 0.08 ± 0.04* 0.06 ± 0.04* Flavobacterium 0.04 ± 0.02 0.06 ± 0.06 0.46 ± 0.41 0.23 ± 0.18 0.03 ± 0.03 0.25 ± 0.18* Fusobacterium 0.69 ± 0.17 0.91 ± 0.32 2.17 ± 1.21* 0.76 ± 0.10 0.48 ± 0.11 1.45 ± 0.30* Haemophilus 0.04 ± 0.02 0.02 ± 0.02 0.00 ± 0.00* 0.00 ± 0.00* 0.00 ± 0.00* 0.00 ± 0.00* Helicobacter 0.11 ± 0.05 0.17 ± 0.15 0.04 ± 0.04 0.04 ± 0.04 0.08 ± 0.04 0.00 ± 0.00* Klebsiella 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.00 ± 0.00 0.10 ± 0.05* 0.04 ± 0.02 Lachnospiraceae IS 0.23 ± 0.20 0.23 ± 0.10 0.00 ± 0.00* 0.06 ± 0.04 0.20 ± 0.09 0.11 ± 0.09 Lactobacillus 5.33 ± 1.05 22.35 ± 10.90* 8.47 ± 2.48 19.46 ± 6.73* 10.33 ± 3.67* 13.69 ± 6.40* Lactococcus 19.52 ± 1.95 15.97 ± 3.73 20.22 ± 3.48 15.12 ± 3.00 18.66 ± 3.24 20.34 ± 3.82 Leuconostoc 32.20 ± 3.88 21.65 ± 3.72* 24.94 ± 0.91* 22.37 ± 4.08* 34.63 ±5.13 27.40 ± 4.13 Megasphaera 0.65 ± 0.13 0.27 ± 0.12* 0.19 ± 0.05* 1.18 ± 0.87 0.25 ± 0.07* 0.55 ± 0.24 Methylobacterium 0.04 ± 0.02 0.00 ± 0.00* 0.00 ± 0.00* 0.00 ± 0.00* 0.05 ± 0.05 0.00 ± 0.00* Microbacterium 0.00 ± 0.00 0.04 ± 0.02* 0.02 ± 0.02 0.02 ± 0.02 0.00 ± 0.00 0.06 ± 0.02* Mitsuokella 0.38 ± 0.14 0.25 ± 0.10 0.06 ± 0.04* 1.98 ± 1.61 0.15 ± 0.08* 0.15 ± 0.07* Mogibacterium 0.06 ± 0.04 0.02 ± 0.02 0.00 ± 0.00* 0.02 ± 0.02 0.00 ± 0.00* 0.02 ± 0.02 Morganella 0.04 ± 0.02 0.00 ± 0.00* 0.00 ± 0.00* 0.02 ± 0.02 0.00 ± 0.00* 0.06 ± 0.04 Neisseria 0.00 ± 0.00 0.04 ± 0.04 0.08 ± 0.05* 0.02 ± 0.02 0.05 ± 0.05 0.02 ± 0.02 Paenibacillus 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 Parabacteroides 0.13 ± 0.08 0.51 ± 0.24* 0.10 ± 0.10 0.15 ± 0.15 0.18 ± 0.14 0.13 ± 0.07 Peptostreptococcaceae IS 0.32 ± 0.05 0.42 ± 0.25 0.23 ± 0.08 0.29 ± 0.19 0.13 ± 0.03* 1.47 ± 0.68* Prevotella 7.20 ± 5.93 3.03 ± 1.38 0.40 ± 0.18* 2.02 ± 1.41 1.32 ± 0.54 1.77 ± 1.42 Propionibacterium 0.36 ± 0.17 0.40 ± 0.29 0.19 ± 0.12 0.04 ± 0.02* 0.74 ± 0.37 0.23 ± 0.03 Proteus 0.00 ± 0.00 0.00 ± 0.00 0.02 ± 0.02 0.04 ± 0.04 0.03 ± 0.03 0.00 ± 0.00 Pseudomonas 0.21 ± 0.16 0.02 ± 0.02* 0.29 ± 0.20 0.08 ± 0.05 0.10 ± 0.07 0.11 ± 0.07 Roseburia 0.08 ± 0.05 0.19 ± 0.11 0.02 ± 0.02 0.06 ± 0.06 0.00 ± 0.00* 0.10 ± 0.07 *

Rubrobacter 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.21 ± 0.16 0.00 ± 0.00 0.02 ± 0.02 128

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Ruminococcaceae IS 0.00 ± 0.00 0.06 ± 0.02* 0.00 ± 0.00 0.00 ± 0.00 0.03 ± 0.03 0.00 ± 0.00 Salmonella 0.02 ± 0.02 0.02 ± 0.02 0.13 ± 0.09 0.11 ± 0.04* 0.03 ± 0.03 0.00 ± 0.00 Sarcina 0.00 ± 0.00 1.88 ± 1.23* 0.00 ± 0.00 0.10 ± 0.05* 0.00 ± 0.00 0.06 ± 0.04* Sphingobacterium 0.04 ± 0.02 0.04 ± 0.02 0.00 ± 0.00* 0.00 ± 0.00* 0.03 ± 0.03 0.00 ± 0.00* Sphingobium 0.00 ± 0.00 0.08 ± 0.05* 0.02 ± 0.02 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 Sphingomonas 0.08 ± 0.03 0.00 ± 0.00* 0.00 ± 0.00* 0.02 ± 0.02* 0.00 ± 0.00* 0.02 ± 0.02* Staphylococcus 0.06 ± 0.04 0.08 ± 0.04 0.04 ± 0.02 0.02 ± 0.02 0.00 ± 0.00* 0.10 ± 0.05 Stenotrophomonas 0.00 ± 0.00 0.00 ± 0.00 0.02 ± 0.02 0.04 ± 0.04 0.03 ± 0.03 0.08 ± 0.05* Streptococcus 10.15 ± 0.94 6.40 ± 1.08* 7.10 ± 1.38* 6.00 ± 1.54* 8.56 ± 1.11 6.02 ± 1.28* Streptophyta 0.53 ± 0.46 0.13 ± 0.08 0.06 ± 0.02 0.02 ± 0.02* 0.00 ± 0.00* 0.11 ± 0.09 Succinivibrio 0.00 ± 0.00 0.38 ± 0.28* 0.00 ± 0.00 0.08 ± 0.05* 0.10 ± 0.05* 0.06 ± 0.04* Sulfurospirillum 0.00 ± 0.00 0.02 ± 0.02 0.00 ± 0.00 0.00 ± 0.00 0.08 ± 0.00* 0.04 ± 0.04 Sutterella 0.08 ± 0.03 0.15 ± 0.10 0.19 ± 0.05* 0.04 ± 0.04 0.08 ± 0.04 0.15 ± 0.06 Tessaracoccus 0.11 ± 0.05 0.02 ± 0.02* 0.02 ± 0.02* 0.00 ± 0.00* 0.05 ± 0.03 0.08 ± 0.03 Thermus 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.10 ± 0.04* 0.03 ± 0.03 0.00 ± 0.00 TM7 genera IS 0.00 ± 0.00 0.00 ± 0.00 0.02 ± 0.02 0.06 ± 0.04* 0.03 ± 0.03 0.06 ± 0.04* Turicibacter 0.02 ± 0.02 2.97 ± 2.67* 0.02 ± 0.02 0.11 ± 0.04* 0.03 ± 0.03 0.69 ± 0.39* Uruburuella 0.00 ± 0.00 0.02 ± 0.02 0.04 ± 0.02* 0.04 ± 0.02* 0.00 ± 0.00 0.00 ± 0.00 Veillonella 1.31 ± 0.07 1.18 ± 0.34 2.44 ± 1.07 1.35 ± 0.23 1.52 ± 0.31 1.85 ± 0.23* Weissella 4.70 ± 0.84 3.96 ± 0.89 5.06 ± 1.00 4.84 ± 1.00 5.28 ± 0.51 7.48 ± 1.42* aThe detection limit was 0.0008 and this value was used to calculate the p-value. *Genera that differ from the control piglets.

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Figure S1. Discrimination between piglet ileum digesta and Peyer’s patch microbiotas. The diagram shows the single axis result of between group analysis applied to correspondence analysis. The five most dominant genera present in either the ileum digesta (Lactobacillus, Escherichia, Sarcina, Turicibacter, and Peptostreptococcaceae Incertae Sedis) or Peyer’s patch (Leuconostoc, Lactococcus, Streptococcus, Acinetobacter, and Weissella) microbiotas have been included. Error bars are used to represent genus coordinates from the correspondence analysis.

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Table S1. Bacterial genera that differ between the control and L. casei 32G-fed piglets at 1.5 h after the last dose. Percentage (mean ± SE) Genera Digestaa Peyer’s patchb Control L. casei 32G p-value Control L. casei 32G p-value Acidaminococcus† 0.40 ± 0.17 0.11 ± 0.07 0.04 0.23 ± 0.20 0.06 ± 0.04 0.16 Actinobacillus†§ 3.26 ± 1.54 6.84 ± 1.49 0.03 0.23 ± 0.05 1.47 ± 1.16 0.12 Allobaculum§ 0.02 ± 0.02 0.02 ± 0.02 0.63 0.00 ± 0.00 0.06 ± 0.04 0.04 Anaerobiospirillum§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.06 ± 0.04 0.00 ± 0.00 0.03 Anaerovorax† 0.10 ± 0.08 0.00 ± 0.00 0.09 0.02 ± 0.02 0.00 ± 0.00 0.11 Arthrobacter§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.04 ± 0.02 0.04 Bacillus d§ 0.03 ± 0.03 0.00 ± 0.00 0.14 0.06 ± 0.04 0.00 ± 0.00 0.03 Bacteroides† 0.03 ± 0.02 0.14 ± 0.07 0.04 0.30 ± 0.08 0.30 ± 0.11 0.51 Clostridium§ 2.66 ± 1.00 2.65 ± 1.79 0.51 0.10 ± 0.06 2.21 ± 2.01 0.12 Cupriavidus§ 0.00 ± 0.00 0.02 ± 0.02 0.10 0.06 ± 0.04 0.00 ± 0.00 0.03 Desulfovibrio† 0.03 ± 0.02 0.00 ± 0.00 0.01 0.00 ± 0.00 0.02 ± 0.02 0.11 Dialister†§ 0.54 ± 0.23 0.00 ± 0.00 0.00 0.15 ± 0.05 0.00 ± 0.00 0.00 Enterococcus§ 0.22 ± 0.20 0.13 ± 0.09 0.29 0.84 ± 0.14 0.44 ± 0.05 0.00 Escherichia†§ 3.18 ± 0.84 7.97 ± 3.76 0.07 1.45 ± 0.20 2.28 ± 0.51 0.04 Fusobacterium† 0.03 ± 0.02 0.16 ± 0.08 0.04 0.69 ± 0.17 0.91 ± 0.32 0.26 Haemophilus† 0.00 ± 0.00 0.05 ± 0.03 0.03 0.04 ± 0.02 0.02 ± 002 0.23 Lactobacillus§ 25.36 ± 11.61 45.36 ± 14.76 0.11 5.33 ± 1.05 22.35 ± 10.90 0.05 Leuconostoc§ 4.99 ± 4.05 1.30 ± 0.92 0.15 32.20 ± 3.88 21.65 ± 3.72 0.01 Megasphaera†§ 9.89 ± 8.04 0.03 ± 0.03 0.09 0.65 ± 0.13 0.27 ± 0.12 0.01 Methylobacterium§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.04 ± 0.02 0.00 ± 0.00 0.03 Microbacterium§ 0.02 ± 0.02 0.02 ± 0.02 0.46 0.00 ± 0.00 0.04 ± 0.02 0.04 Mitsuokella† 2.96 ± 1.65 0.08 ± 0.05 0.02 0.38 ± 0.14 0.25 ± 0.10 0.19 Moraxella† 0.05 ± 0.03 0.00 ± 0.00 0.05 0.00 ± 0.00 0.02 ± 0.02 0.15 Morganella§ 0.02 ± 0.02 0.00 ± 0.00 0.11 0.04 ± 0.02 0.00 ± 0.00 0.02 Olsenella† 0.13 ± 0.09 0.00 ± 0.00 0.06 0.04 ± 0.04 0.00 ± 0.00 0.12 § Parabacteroides 0.16 ± 0.14 0.10 ± 0.10 0.36 0.13 ± 0.08 0.51 ± 0.24 0.06 131

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Peptostreptococcaceae IS† 10.27 ± 4.96 0.43 ± 0.23 0.02 0.32 ± 0.05 0.42 ± 0.25 0.33 Pseudomonas§ 0.03 ± 0.03 0.03 ± 0.03 0.58 0.21 ± 0.16 0.02 ± 0.02 0.08 Ruminococcaceae IS§ 0.02 ± 0.02 0.02 ± 0.02 0.45 0.00 ± 0.00 0.06 ± 0.02 0.00 Sarcina†§ 0.02 ± 0.02 20.06 ± 16.90 0.10 0.00 ± 0.00 1.88 ± 1.23 0.05 Sphingobium§ 0.02 ± 0.02 0.00 ± 0.00 0.11 0.00 ± 0.00 0.08 ± 0.05 0.05 Sphingomonas§ 0.02 ± 0.02 0.00 ± 0.00 0.11 0.08 ± 0.03 0.00 ± 0.00 0.00 Streptococcus†§ 0.57 ± 0.23 1.36 ± 0.35 0.02 10.15 ± 0.94 6.40 ± 1.08 0.00 Succinivibrio§ 0.02 ± 0.02 0.13 ± 0.13 0.15 0.00 ± 0.00 0.38 ± 0.28 0.06 Sulfurospirillum† 0.05 ± 0.03 0.00 ± 0.00 0.03 0.00 ± 0.00 0.02 ± 0.02 0.15 Tessaracoccus†§ 0.00 ± 0.00 0.03 ± 0.02 0.02 0.11 ± 0.05 0.02 ± 0.02 0.01 Turicibacter§ 1.31 ± 0.94 4.09 ± 2.20 0.08 0.02 ± 0.02 2.97 ± 2.67 0.12 Yersinia† 0.02 ± 0.02 0.13 ± 0.07 0.04 0.00 ± 0.00 0.00 ± 0.00 N/A aThe detection limit was 0.0006 and this value was used to calculate the p-value. bThe detection limit was 0.0008 and this value was used to calculate the p-value. †Genera that differ in the ileum digesta microbiota. §Genera that differ in the ileum Peyer’s patch microbiota.

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Table S2. Bacterial genera that differ between the control and L. casei 32G-fed piglets at 6 h after the last dose. Genera Percentage (mean ± SE) Digestaa Peyer’s patchb Control L. casei 32G p-value Control L. casei 32G p-value Anaerobiospirillum§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.06 ± 0.04 0.00 ± 0.00 0.03 Anaerovorax† 0.10 ± 0.08 0.00 ± 0.00 0.09 0.02 ± 0.02 0.04 ± 0.04 0.31 Bacillus d§ 0.03 ± 0.03 0.00 ± 0.00 0.13 0.06 ± 0.04 0.00 ± 0.00 0.03 Bacteroides† 0.03 ± 0.02 0.35 ± 0.15 0.00 0.30 ± 0.08 0.34 ± 0.12 0.39 Citrobacter§ 0.89 ± 0.66 0.70 ± 0.49 0.38 2.15 ± 0.21 3.39 ± 0.91 0.07 Clostridium† 2.66 ± 1.00 0.00 ± 0.00 0.00 0.10 ± 0.06 0.08 ± 0.00 0.37 Cupriavidus§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.06 ± 0.04 0.00 ± 0.00 0.05 Desulfovibrio†§ 0.03 ± 0.02 0.00 ± 0.00 0.04 0.00 ± 0.00 0.06 ± 0.04 0.04 Dialister†§ 0.54 ± 0.23 0.02 ± 0.02 0.01 0.15 ± 0.05 0.04 ± 0.04 0.02 Enterobacter§ 0.00 ± 0.00 0.02 ± 0.02 0.16 0.02 ± 0.02 0.08 ± 0.03 0.04 Escherichia†§ 3.18 ± 0.84 33.81 ± 8.47 0.00 1.45 ± 0.20 11.46 ± 4.98 0.01 Faecalibacterium§ 0.63 ± 0.59 0.10 ± 0.10 0.16 0.40 ± 0.26 0.04 ± 0.02 0.07 Flavobacterium† 0.03 ± 0.02 0.00 ± 0.00 0.03 0.04 ± 0.02 0.46 ± 0.41 0.11 Fusobacterium†§ 0.03 ± 0.02 6.29 ± 3.48 0.03 0.69 ± 0.17 2.17 ± 1.21 0.08 Haemophilus§ 0.00 ± 0.00 0.02 ± 0.02 0.12 0.04 ± 0.02 0.00 ± 0.00 0.03 Klebsiella† 0.00 ± 0.00 0.05 ± 0.03 0.03 0.02 ± 0.02 0.02 ± 0.02 0.58 Lachnospiraceae IS§ 0.54 ± 0.54 0.08 ± 0.06 0.17 0.23 ± 0.20 0.00 ± 0.00 0.10 Leuconostoc§ 4.99 ± 4.05 2.72 ± 1.76 0.30 32.20 ± 3.88 24.94 ± 0.91 0.02 Megasphaera†§ 9.89 ± 8.04 0.27 ± 0.17 0.10 0.65 ± 0.13 0.19 ± 0.05 0.00 Methylobacterium§ 0.00 ± 0.00 0.02 ± 0.02 0.16 0.04 ± 0.02 0.00 ± 0.00 0.03 Mitsuokella†§ 2.96 ± 1.65 0.33 ± 0.20 0.05 0.38 ± 0.14 0.06 ± 0.04 0.00 Mogibacterium§ 0.05 ± 0.05 0.02 ± 0.02 0.24 0.06 ± 0.04 0.00 ± 0.00 0.03 Morganella§ 0.02 ± 0.02 0.02 ± 0.02 0.55 0.04 ± 0.02 0.00 ± 0.00 0.05 Neisseria§ 0.22 ± 0.14 0.13 ± 0.04 0.23 0.00 ± 0.00 0.08 ± 0.05 0.06 Peptostreptococcaceae IS† 10.27 ± 4.96 0.00 ± 0.00 0.01 0.32 ± 0.05 0.23 ± 0.08 0.13 Prevotella§ 17.44 ± 16.35 5.18 ± 4.81 0.21 7.20 ± 5.93 0.40 ± 0.18 0.10 133

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Salmonella† 0.00 ± 0.00 0.11 ± 0.05 0.01 0.02 ± 0.02 0.13 ± 0.09 0.08 Sphingobacterium§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.04 ± 0.02 0.00 ± 0.00 0.03 Sphingomonas§ 0.02 ± 0.02 0.00 ± 0.00 0.12 0.08 ± 0.03 0.00 ± 0.00 0.00 Streptococcus§ 0.57 ± 0.23 0.35 ± 0.19 0.21 10.15 ± 0.94 7.10 ± 1.38 0.02 Sulfurospirillum† 0.05 ± 0.03 0.00 ± 0.00 0.05 0.00 ± 0.00 0.00 ± 0.00 N/A Sutterella†§ 0.03 ± 0.03 0.35 ± 0.17 0.02 0.08 ± 0.03 0.19 ± 0.05 0.02 Tessaracoccus§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.11 ± 0.05 0.02 ± 0.02 0.02 Turicibacter† 1.31 ± 0.94 0.00 ± 0.00 0.06 0.02 ± 0.02 0.02 ± 0.02 0.60 Uruburuella§ 0.02 ± 0.02 0.00 ± 0.00 0.12 0.00 ± 0.00 0.04 ± 0.02 0.03 Veillonella† 0.33 ± 0.14 4.75 ± 2.07 0.01 1.31 ± 0.07 2.44 ± 1.07 0.12 Vogesella† 0.03 ± 0.02 0.00 ± 0.00 0.04 0.02 ± 0.02 0.00 ± 0.00 0.14 aThe detection limit was 0.0006 and this value was used to calculate the p-value. bThe detection limit was 0.0008 and this value was used to calculate the p-value. †Genera that differ in the ileum digesta microbiota. §Genera that differ in the ileum Peyer’s patch microbiota.

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Table S3. Bacterial genera that differ between the control and L. casei 32G-fed piglets at 12 h after the last administration. Genera Percentage (mean ± SE) Digestaa Peyer’s patchb Control L. casei 32G p-value Control L. casei 32G p-value Acinetobacter§ 1.43 ± 1.22 0.41 ± 0.33 0.19 5.50 ± 2.02 2.57 ± 0.89 0.05 Actinobacillus†§ 3.26 ± 1.54 0.02 ± 0.02 0.01 0.23 ± 0.05 0.08 ± 0.03 0.00 Anaerobiospirillum§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.06 ± 0.04 0.00 ± 0.00 0.03 Anaerovorax† 0.10 ± 0.08 0.00 ± 0.00 0.09 0.02 ± 0.02 0.00 ± 0.00 0.14 Aquabacterium† 0.05 ± 0.03 0.00 ± 0.00 0.05 0.02 ± 0.02 0.00 ± 0.00 0.15 Bacillus d§ 0.03 ± 0.03 0.00 ± 0.00 0.16 0.06 ± 0.04 0.00 ± 0.00 0.03 Bacteroides†§ 0.03 ± 0.02 0.13 ± 0.07 0.06 0.30 ± 0.08 0.10 ± 0.04 0.01 Blastomonas§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.06 ± 0.04 0.00 ± 0.00 0.03 Clostridium† 2.66 ± 1.00 0.27 ± 0.15 0.01 0.10 ± 0.06 0.08 ± 0.08 0.42 Curvibacter§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.08 ± 0.05 0.07 Dialister†§ 0.54 ± 0.23 0.00 ± 0.00 0.00 0.15 ± 0.05 0.04 ± 0.04 0.03 Erysipelotrichaceae IS†§ 0.05 ± 0.03 0.00 ± 0.00 0.04 0.11 ± 0.07 0.00 ± 0.00 0.04 Escherichia†§ 3.18 ± 0.84 38.70 ± 18.14 0.01 1.45 ± 0.20 13.44 ± 4.72 0.00 Faecalibacterium†§ 0.63 ± 0.59 0.00 ± 0.00 0.11 0.40 ± 0.26 0.06 ± 0.04 0.06 Flavobacterium† 0.03 ± 0.02 0.00 ± 0.00 0.03 0.04 ± 0.02 0.23 ± 0.18 0.12 Fusobacterium† 0.03 ± 0.02 1.19 ± 1.13 0.11 0.69 ± 0.17 0.76 ± 0.10 0.34 Haemophilus§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.04 ± 0.02 0.00 ± 0.00 0.03 Lactobacillus§ 25.36 ± 11.61 41.56 ± 13.53 0.14 5.33 ± 1.05 19.46 ± 6.73 0.01 Leuconostoc§ 4.99 ± 4.05 3.56 ± 3.19 0.34 32.20 ± 3.88 22.37 ± 4.08 0.02 Methylobacterium§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.04 ± 0.02 0.00 ± 0.00 0.03 Neisseria† 0.22 ± 0.14 0.00 ± 0.00 0.03 0.00 ± 0.00 0.02 ± 0.02 0.15 Olsenella† 0.13 ± 0.09 0.00 ± 0.00 0.06 0.04 ± 0.04 0.02 ± 0.02 0.32 Parabacteroides† 0.16 ± 0.14 0.00 ± 0.00 0.10 0.13 ± 0.08 0.15 ± 0.15 0.43 Peptostreptococcaceae IS† 10.27 ± 4.96 0.71 ± 0.63 0.01 0.32 ± 0.05 0.29 ± 0.19 0.40 Prevotella† 17.44 ± 16.35 0.05 ± 0.02 0.11 7.20 ± 5.93 2.02 ± 1.41 0.17 §

Propionibacterium 0.06 ± 0.06 0.00 ± 0.00 0.16 0.36 ± 0.17 0.04 ± 0.02 0.01 135

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Roseburia† 0.67 ± 0.64 0.00 ± 0.00 0.11 0.08 ± 0.05 0.06 ± 0.06 0.41 Rubrobacter§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.21 ± 0.16 0.06 Salmonella†§ 0.00 ± 0.00 0.27 ± 0.12 0.00 0.02 ± 0.02 0.11 ± 0.04 0.00 Sarcina†§ 0.02 ± 0.02 0.33 ± 0.24 0.09 0.00 ± 0.00 0.10 ± 0.05 0.01 Sphingobacterium§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.04 ± 0.02 0.00 ± 0.00 0.03 Sphingomonas§ 0.02 ± 0.02 0.00 ± 0.00 0.15 0.08 ± 0.03 0.02 ± 0.02 0.03 Streptococcus†§ 0.57 ± 0.23 2.96 ± 1.40 0.04 10.15 ± 0.94 6.00 ± 1.54 0.00 Streptophyta§ 0.03 ± 0.03 0.02 ± 0.02 0.29 0.53 ± 0.46 0.02 ± 0.02 0.09 Subdoligranulum† 0.19 ± 0.13 0.00 ± 0.00 0.06 0.25 ± 0.17 0.04 ± 0.04 0.09 Succinivibrio§ 0.02 ± 0.02 0.00 ± 0.00 0.14 0.00 ± 0.00 0.08 ± 0.05 0.06 Sulfurospirillum† 0.05 ± 0.03 0.00 ± 0.00 0.04 0.00 ± 0.00 0.00 ± 0.00 N/A Tessaracoccus§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.11 ± 0.05 0.00 ± 0.00 0.01 Thermus§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.10 ± 0.04 0.00 TM7 genera IS§ 0.00 ± 0.00 0.02 ± 0.02 0.12 0.00 ± 0.00 0.06 ± 0.04 0.03 Turicibacter§ 1.31 ± 0.94 0.68 ± 0.53 0.24 0.02 ± 0.02 0.11 ± 0.04 0.00 Uruburuella§ 0.02 ± 0.02 0.02 ± 0.02 0.59 0.00 ± 0.00 0.04 ± 0.02 0.03 Veillonella† 0.33 ± 0.14 0.74 ± 0.20 0.02 1.31 ± 0.07 1.35 ± 0.23 0.41 Vogesella† 0.03 ± 0.02 0.00 ± 0.00 0.03 0.02 ± 0.02 0.06 ± 0.04 0.13 aThe detection limit was 0.0006 and this value was used to calculate the p-value. bThe detection limit was 0.0008 and this value was used to calculate the p-value. †Genera that differ in the ileum digesta microbiota. §Genera that differ in the ileum Peyer’s patch microbiota.

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Table S4. Bacterial genera that differ between the control and L. casei 32G-fed piglets at 24 h after the last administration. Genera Percentage (mean ± SE) Digestaa Peyer’s patchb Control L. casei 32G p-value Control L. casei 32G p-value Acidaminococcus†§ 0.40 ± 0.17 0.05 ± 0.03 0.01 0.23 ± 0.20 0.00 ± 0.00 0.18 Acinetobacter§ 1.43 ± 1.22 0.92 ± 0.69 0.35 5.50 ± 2.02 2.87 ± 0.55 0.11 Actinobacillus† 3.26 ± 1.54 0.08 ± 0.05 0.01 0.23 ± 0.05 0.15 ± 0.08 0.18 Anaerobiospirillum§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.06 ± 0.04 0.00 ± 0.00 0.05 Bacillus d§ 0.03 ± 0.03 0.00 ± 0.00 0.14 0.06 ± 0.04 0.00 ± 0.00 0.07 Bacteroides† 0.03 ± 0.02 0.97 ± 0.90 0.10 0.30 ± 0.08 0.43 ± 0.07 0.09 Blastomonas† 0.00 ± 0.00 0.03 ± 0.02 0.02 0.06 ± 0.04 0.05 ± 0.03 0.43 Citrobacter§ 0.89 ± 0.66 0.55 ± 0.47 0.34 2.15 ± 0.21 1.47 ± 0.35 0.01 Clostridium†§ 2.66 ± 1.00 0.19 ± 0.19 0.00 0.10 ± 0.06 0.00 ± 0.00 0.05 Cupriavidus§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.06 ± 0.04 0.00 ± 0.00 0.08 Dialister†§ 0.54 ± 0.23 0.03 ± 0.03 0.01 0.15 ± 0.05 0.00 ± 0.00 0.01 Escherichia† 3.18 ± 0.84 26.87 ± 20.76 0.09 1.45 ± 0.20 7.51 ± 6.33 0.08 Faecalibacterium§ 0.63 ± 0.59 0.41 ± 0.37 0.36 0.40 ± 0.26 0.08 ± 0.04 0.15 Haemophilus§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.04 ± 0.02 0.00 ± 0.00 0.04 Klebsiella§ 0.00 ± 0.00 0.02 ± 0.02 0.12 0.02 ± 0.02 0.10 ± 0.05 0.03 Lactobacillus§ 25.36 ± 11.61 48.40 ± 23.78 0.18 5.33 ± 1.05 10.33 ± 3.67 0.04 Megasphaera†§ 9.89 ± 8.04 0.35 ± 0.23 0.09 0.65 ± 0.13 0.25 ± 0.07 0.00 Mitsuokella†§ 2.96 ± 1.65 0.25 ± 0.14 0.03 0.38 ± 0.14 0.15 ± 0.08 0.07 Mogibacterium§ 0.05 ± 0.05 0.05 ± 0.05 0.60 0.06 ± 0.04 0.00 ± 0.00 0.07 Moraxella† 0.05 ± 0.03 0.00 ± 0.00 0.04 0.00 ± 0.00 0.00 ± 0.00 N/A Morganella§ 0.02 ± 0.02 0.02 ± 0.02 0.13 0.04 ± 0.02 0.00 ± 0.00 0.03 Neisseria† 0.22 ± 0.14 0.00 ± 0.00 0.03 0.00 ± 0.00 0.05 ± 0.05 0.07 Peptostreptococcaceae IS†§ 10.27 ± 4.96 0.00 ± 0.00 0.01 0.32 ± 0.05 0.13 ± 0.03 0.00 Roseburia§ 0.67 ± 0.64 0.35 ± 0.27 0.31 0.08 ± 0.05 0.00 ± 0.00 0.11 Sphingomonas§ 0.02 ± 0.02 0.00 ± 0.00 0.14 0.08 ± 0.03 0.00 ± 0.00 0.01 Staphylococcus§ 0.05 ± 0.05 0.00 ± 0.00 0.13 0.06 ± 0.04 0.00 ± 0.00 0.07 137

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Streptophyta§ 0.03 ± 0.03 0.06 ± 0.04 0.24 0.53 ± 0.46 0.00 ± 0.00 0.14 Succinivibrio§ 0.02 ± 0.02 0.11 ± 0.09 0.10 0.00 ± 0.00 0.10 ± 0.05 0.00 Sulfurospirillum§ 0.05 ± 0.03 0.05 ± 0.05 0.50 0.00 ± 0.00 0.08 ± 0.00 0.00 aThe detection limit was 0.0006 and this value was used to calculate the p-value. bThe detection limit was 0.0008 and this value was used to calculate the p-value. †Genera that differ in the ileum digesta microbiota. §Genera that differ in the ileum Peyer’s patch microbiota.

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Table S5. Bacterial genera that differ between the control and L. casei 32G-fed piglets at 72 h after the last administration. Genera Percentage (mean ± SE) Digestaa Peyer’s patchb Control L. casei 32G p-value Control L. casei 32G p-value Acidaminococcus† 0.40 ± 0.17 0.06 ± 0.06 0.02 0.23 ± 0.20 0.21 ± 0.21 0.38 Actinobacillus†§ 3.26 ± 1.54 0.17 ± 0.17 0.01 0.23 ± 0.05 0.10 ± 0.04 0.01 Allobaculum§ 0.02 ± 0.02 0.05 ± 0.05 0.21 0.00 ± 0.00 0.04 ± 0.02 0.03 Anaerobiospirillum§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.06 ± 0.04 0.00 ± 0.00 0.05 Bacillus d§ 0.03 ± 0.03 0.00 ± 0.00 0.11 0.06 ± 0.04 0.00 ± 0.00 0.04 Bacteroides§ 0.03 ± 0.02 0.54 ± 0.54 0.16 0.30 ± 0.08 0.13 ± 0.05 0.02 Blastomonas§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.06 ± 0.04 0.00 ± 0.00 0.04 Corynebacterium§ 0.02 ± 0.02 0.02 ± 0.02 0.62 0.06 ± 0.02 0.00 ± 0.00 0.00 Dialister† 0.54 ± 0.23 0.00 ± 0.00 0.00 0.15 ± 0.05 0.10 ± 0.10 0.30 Enhydrobacter§ 0.10 ± 0.10 0.35 ± 0.27 0.15 0.36 ± 0.08 0.88 ± 0.41 0.08 Erysipelotrichaceae IS§ 0.05 ± 0.03 0.16 ± 0.16 0.21 0.11 ± 0.07 0.00 ± 0.00 0.05 Faecalibacterium§ 0.63 ± 0.59 0.14 ± 0.14 0.19 0.40 ± 0.26 0.06 ± 0.04 0.07 Flavobacterium§ 0.03 ± 0.02 0.10 ± 0.05 0.11 0.04 ± 0.02 0.25 ± 0.18 0.10 Fusobacterium†§ 0.03 ± 0.02 0.00 ± 0.00 0.03 0.69 ± 0.17 1.45 ± 0.30 0.01 Haemophilus§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.04 ± 0.02 0.00 ± 0.00 0.04 Helicobacter§ 0.30 ± 0.30 0.06 ± 0.06 0.22 0.11 ± 0.05 0.00 ± 0.00 0.01 Lactobacillus§ 25.36 ± 11.61 50.86 ± 22.30 0.14 5.33 ± 1.05 13.69 ± 6.40 0.07 Megasphaera† 9.89 ± 8.04 0.33 ± 0.33 0.10 0.65 ± 0.13 0.55 ± 0.24 0.37 Methylobacterium§ 0.00 ± 0.00 0.03 ± 0.03 0.11 0.04 ± 0.02 0.00 ± 0.00 0.04 Microbacterium§ 0.02 ± 0.02 0.00 ± 0.00 0.14 0.00 ± 0.00 0.06 ± 0.02 0.00 Mitsuokella†§ 2.96 ± 1.65 0.19 ± 0.19 0.03 0.38 ± 0.14 0.15 ± 0.07 0.05 Neisseria† 0.22 ± 0.14 0.00 ± 0.00 0.04 0.00 ± 0.00 0.02 ± 0.02 0.13 Olsenella† 0.13 ± 0.09 0.00 ± 0.00 0.06 0.04 ± 0.04 0.00 ± 0.00 0.11 Peptostreptococcaceae IS§ 10.27 ± 4.96 5.53 ± 3.88 0.21 0.32 ± 0.05 1.47 ± 0.68 0.03 Salmonella† 0.00 ± 0.00 0.05 ± 0.02 0.00 0.02 ± 0.02 0.00 ± 0.00 0.14 Sarcina§ 0.02 ± 0.02 0.22 ± 0.20 0.13 0.00 ± 0.00 0.06 ± 0.04 0.04 139

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Sphingobacterium§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.04 ± 0.02 0.00 ± 0.00 0.04 Sphingomonas§ 0.02 ± 0.02 0.00 ± 0.00 0.14 0.08 ± 0.03 0.02 ± 0.02 0.03 Stenotrophomonas§ 0.02 ± 0.02 0.00 ± 0.00 0.14 0.00 ± 0.00 0.08 ± 0.05 0.04 Streptococcus§ 0.57 ± 0.23 0.54 ± 0.28 0.45 10.15 ± 0.94 6.02 ± 1.28 0.00 Succinivibrio§ 0.02 ± 0.02 0.29 ± 0.29 0.14 0.00 ± 0.00 0.06 ± 0.04 0.04 Sulfurospirillum† 0.05 ± 0.03 0.00 ± 0.00 0.04 0.00 ± 0.00 0.04 ± 0.04 0.12 TM7 genera IS§ 0.00 ± 0.00 0.00 ± 0.00 N/A 0.00 ± 0.00 0.06 ± 0.04 0.04 Turicibacter§ 1.31 ± 0.94 14.02 ± 12.95 0.13 0.02 ± 0.02 0.69 ± 0.39 0.03 Veillonella§ 0.33 ± 0.14 0.16 ± 0.10 0.10 1.31 ± 0.07 1.85 ± 0.23 0.01 Vogesella† 0.03 ± 0.02 0.00 ± 0.00 0.04 0.02 ± 0.02 0.08 ± 0.08 0.21 Weissella§ 5.96 ± 5.05 1.33 ± 1.01 0.15 4.70 ± 0.84 7.48 ± 1.42 0.02 aThe detection limit was 0.0006 and this value was used to calculate the p-value. bThe detection limit was 0.0008 and this value was used to calculate the p-value. †Genera that differ in the ileum digesta microbiota. §Genera that differ in the ileum Peyer’s patch microbiota.

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CHAPTER 5

Dose-dependent impact of Lactobacillus casei 32G on the mouse cecum microbiota

Kanokwan Tandee,1 Busra Aktas,1 Travis De Wolfe,1,2 Megan Duster,3 Simone Warrack,3

Benjamin Darien,2 Nasia Safdar,3 and James Steele1

Department of Food Science1; School of Veterinary Medicine2; and School of Medicine and

Public Health,3 University of Wisconsin-Madison 142

Abstract

Previous research conducted by our group demonstrated that administration of

Lactobacillus casei 32G to piglets resulted in a microbe specific restructuring of the ileum digesta and tissue adherent microbiotas. However, the specific alterations observed differed between the two studies and one of the differences between the two studies was the dose of

32G administered. The objective of this study was to use mice to investigate if 32G-induced changes in the gastrointestinal tract microbiota are dose dependent. L. casei 32G was administered to four week old BALB/c male mice by oral gavage at doses of 106 (low dose),

107 (medium dose), and 108 (high dose) CFU/day for seven days. Mice were sacrificed 0, 3,

6, 12, and 24 h after the last dose and the contents of the cecum collected. The numbers of lactobacilli, coliforms, and clostridia in the cecum digesta samples were determined by plating on Rogosa SL, Violet Red Bile, and Brucella agar plates, respectively. Additionally, the samples were analyzed for the composition of their microbiota by automated ribosomal intergenic spacer analysis (ARISA). The results indicate that 32G administration at the medium and high doses resulted in increases in numbers of lactobacilli present in the cecum digesta. In general, none of the doses examined influenced the numbers of clostridia and coliforms. Administration of the low 32G dose resulted in significant changes in the mouse cecum microbiota, in comparison to the control, medium dose, and high dose microbiotas.

Administration of the medium and high 32G doses resulted in microbiota that were significantly different than the control and low dose microbiota at most time points but, in 143 general, did not differ from each other. This study demonstrated that dose dependent changes occur in the cecum microbiota of mice upon administration of L. casei 32G. 144

Introduction

The mammalian gastrointestinal tract (GIT) is a series of anatomically distinct compartments that provide a number of distinct environments for microbial colonization

(17). The GIT contains approximately 1014 bacterial cells and these organisms are thought to influence the health in a number of important ways. Health implications of the GIT microbiota include production of nutrients (e.g. vitamins and organic acids), resistance to pathogenic microorganisms, and the development of the immune system (17). Resistance to infections by microbial pathogens is thought to occur by competitive exclusion and by immune modulation. Mechanisms of competitive exclusion include competition for nutrients and adherence sites, as well as the production of inhibitory compounds, such as bacteriocins and organic acids (1, 6, 11, 14). Immunomodulation can result in increased resistance via the production of defensins and sIgA (18). An approach to modulate the composition of the GIT microbiota is through the administration of probiotics. Probiotics, as defined by FAO and

WHO are ―live microorganisms which when administered in adequate amounts confer a health benefit on the host‖ (5). This definition highlights the importance of dose in probiotic efficacy.

The efficacy of probiotics has been demonstrated to be dose-dependent in multiple studies examining enhanced resistance to pathogens. Yang et al. (19) demonstrated that prevention of gastroenteritis induced by Vibrio parahaemolyticus, in a mouse model, was dose dependent for the four strains of lactobacilli examined. All of the strains were effective when administered at 5 x 108 CFU/day for seven days, while none of the strains were 145 effective when administered at 5 x 106 CFU/day for seven days. At an intermediate dose, 5 x

107 CFU/day for seven days, two of the four strains examined were effective. Li et al. (13) examined the dose-dependent ability of a Lactobacillus rhamnosus strain to prevent infection by an enterotoxigenic Escherichia coli (ETEC) strain in a piglet model. While both doses

(1010 or 1012 CFU/day for seven days) were effective at preventing ETEC-induced diarrhea, the higher dose was observed to result in increased incidence of diarrhea prior to ETEC challenge. Additionally, the two doses were shown to differ in their impact on the composition of the ileal microbiota and in their influence on the expression of TLR2, TLR9,

NOD1 and TNF-α. Goa et al. (9) examined the dose-dependent ability of a commercial probiotic capsule containing both Lactobacillus acidophilus and Lactobacillus casei strain to prevent Clostridium difficile-associated diarrhea (CDAD) in adult human inpatients. The treatment groups received either one capsule or two capsules 2 h after breakfast each day, starting within 36 h of initiating antibiotic therapy and continuing until five days after their last antibiotic treatment. Each capsule contained a total of 5 x 1010 CFU. The incidence of

CDAD in the placebo, low, and high dose groups were determined to be 23.8, 9.4, and 1.2%, respectively. These studies demonstrate that probiotic efficacy in reducing gastrointestinal pathogen infections are dose dependent for three different pathogens in three different hosts.

While the results from the piglet ETEC study suggest that these differences may be due to dose-dependent immune modulation, the mechanism(s) responsible for these dose-dependent results remain unknown.

Previously, our research group has conducted two studies examining L. casei 32G’s ability to modulate the ileum microbiota of piglets (Chapters 3 and 4). In both studies, L. 146 casei 32G resulted in a microbe-specific restructuring, not simply diluting, of the ileum digesta and tissue adherent microbiotas. The specific alterations (at the genus level) in the ileum microbiotas differed between the two studies. One of the differences between the two studies was that different L. casei 32G doses were utilized and hence the influence of dose on microbiota composition is the primary objective of this study. 147

Materials and methods

Bacterial strain. L. casei 32G was maintained at -80°C in MRS broth (BD Difco,

Sparks, MD) with 25% (v/v) glycerol (Sigma-Aldrich, St. Louis, MO) as previously described (Chapters 3 and 4). Working cultures were prepared from frozen stocks by two sequential transfers in MRS broth and incubations were conducted statically at 37°C for 24 h and 18 h, respectively. The culture was harvested by centrifugation at 5,000 ×g for 10 min at

25°C. The pellet was resuspended in 0.85% NaCl (w/v) and the optical density at 600 nm

(OD600) determined. A volume of washed cells (based upon the OD600) sufficient to yield a 1 ml cell suspension with an OD600 of 6.0 was harvested by centrifugation at 5,000 ×g and washed with 1 ml of 0.85% NaCl. The resulting pellet was suspended in 1 ml of 0.85% NaCl to obtain a final concentration of 109 CFU/ml. The culture was serially diluted in 0.85%

NaCl to reach concentrations of 108 and 107 CFU/ml and kept on ice until administered.

Animals. All procedures involving mice were conducted under the protocol

#V01548-0-02-12 approved by the Animal Care and Use Committee of University of

Wisconsin-Madison. Sixty BALB/c male mice aged four weeks old were purchased from

Harlan Laboratories (Madison, WI) and group housed in sterile cages at University of

Wisconsin-Madison Animal Health and Biomedical Science facility. Housing conditions were controlled at 25°C, 20-44% relative humidity with a 12 h light/dark cycle. Mice were fed ad libitum water and diet (2018 Teklad Global 18% Protein Rodent Diet, Harlan

Laboratories) throughout the study. After four days of housing, mice were randomized into four groups (15 mice per group); each group was administered daily 100 µl of either 0.85% 148

NaCl (control), 107, 108, or 109 CFU/ml of L. casei 32G by oral gavage for seven days.

Therefore, the delivered doses were 106 (low dose), 107 (medium dose), and 108 (high dose)

CFU/day.

Sample collection. Three mice were euthanized by CO2 asphyxiation at 0, 3, 6, 12, and 24 h after the last administration. The cecum was dissected and the 10-1 dilution of digesta was obtained by flushing the cecum with 1.5 ml of phosphate buffered saline, which was further diluted with 0.85% NaCl to 10-5. The 10-1 and 10-2 dilutions were plated using the standard pour-plated method, while 10-2 to 10-5 dilutions were plated using the drop- plated method (15). The 10-1 dilution of digesta samples was maintained at -20°C for further analysis. Rogosa SL (BD Difco), Violet Red Bile (VRB, BD Difco), and Brucella (BD

Difco) agar plates were utilized to select for Lactobacillus, coliform bacteria, and class

Clostridia, respectively. Agar plates were incubated at 37°C for 48-96 h prior to enumeration. For Brucella agar plates, only colonies of catalase-negative, Gram-positive rods were counted as class Clostridia. To confirm the identity of class Clostridia, eight colonies were randomly selected, boiled for 10 min, and species identified by 16S rRNA sequencing.

16S rRNA sequencing. Partial sequences 16S rRNA genes were amplified using primers 5′-ACT CCT ACG GCA GGC AGC AGT-3′ and 5′-AGG CCC GGG AAC GTA

TTC ACC G-3′. PCR amplifications were performed using iProof High-Fidelity DNA polymerase (Biorad, Hercules, CA) with an iCycler Thermal Cycler (Biorad). PCR conditions were: initial denaturation at 98°C for 3 min; 35 cycles of 98°C for 10 sec, 50°C for 30 sec, and 72°C for 30 sec; final extension at 72°C for 10 min; and holding at 4°C. 20 149

µl reactions with 0.8 µl of cell lysate were prepared according to the manufacturer’s direction. Sequencing reactions were prepared using the same primers and Big Dye

(Biotechnology Center, University of Wisconsin-Madison) with the following conditions: 35 cycles of 94°C for 30 sec, 50°C for 20 sec, and 60°C for 4 min; and holding at 4°C. Products were purified using magnetic beads (Beckman Coulter, Brea, CA) and submitted to the

University of Wisconsin Biotechnology Center for the sequence determination. Partial sequences were manually edited in FinchTV version 1.4.0 (http://www.geospiza.com) and contigs were aligned and assembled in Mega version 4.1 (http://megasoftware.net), yielding the 950-1,000 bp sequences. Closely related species was determined by comparing each sequence to Ribosomal Database Project of type and non-type strains, isolates, near-full- length (> 1,200 bp), and good quality sequences by Seqmatch version 3

(http://rdp.msu.cme.edu).

Automated ribosomal intergenic spacer analysis (ARISA). Total DNA from 0.2 ml of 10-1 dilution digesta samples was isolated using the QIAamp DNA Stool Mini Kit

(Qiagen Sciences, MD). ARISA-PCR reactions were conducted using primer 1406f, 5' -

TGY ACA CAC CGC CCG T - 3', which was labeled with phosphoramidite dye 5-FAM, and primer 23Sr, 5' - GGG TTB CCC CAT TCR G - 3' (7). PCR amplification was performed using iProof High-Fidelity DNA polymerase (Biorad) with an iCycler Thermal Cycler

(Biorad). The PCR conditions utilized for amplification of the 16S-23S spacer region were: initial denaturation at 98°C for 3 min; 35 cycles of 98°C for 10 sec, 55°C for 30 sec, and

72°C for 30 sec; final extension at 72°C for 10 min; and holding at 4°C. 20 µl reactions with

80 ng of genomic DNA were prepared according to iProof High-Fidelity DNA polymerase 150 directions. One µl of PCR products, 0.4 µl of custom 100 - 2,000 bp standard labeled with

Rhodamine X (Bioventures, Murfreesboro, TN), and 10 µl of highly deionized formamide

(Applied Biosystems, Foster City, CA) were submitted to the University of Wisconsin-

Madison Biotechnology Center for capillary electrophoresis in an ABI 3700 Genetic

Analyzer (12). Amplicon sizes were determined by comparison to the internal size standard.

Peak area, which is proportional to DNA quantity, was calculated using PeakScanner

(http://www.appliedbiosystems.com). Peaks with greater than 300 fluorescence units, which were between 300-1,250 bp, were included in the ARISA profiles.

Statistical analysis. Numbers of Lactobacillus, coliform bacteria, and class

Clostridia are presented as log CFU/ml with standard error of mean (SE). Statistical differences among doses (low, medium, and high) and sampling time points (0, 3, 6, 12, and

24 h after the last administration) were determined by Analysis of Variance (ANOVA) followed by Tukey-Gramer grouping (JMP version 9.0.2, SAS Institute, Cary, NC). For the

ARISA data, statistical differences among doses and treatments (interaction between doses and sampling time points) were compared by between group analysis in package ade4 (4) of

R version 2.14.0 (16) as described by de Carcer et al. (3). 151

Results and Discussion

The primary objective of this study was to establish a mouse model for examining the influence of dose on the ability of L. casei 32G to alter the composition of the GIT microbiota. Two previous piglet trials (Chapters 3 and 4) had demonstrated that administration of L. casei 32G resulted in a microbe-specific restructuring, not simply diluting, of the ileum digesta and tissue adherent microbiotas. However, the specific alterations (at the genus level) in the ileum microbiotas differed between the two studies.

One of the differences between the two studies was that different L. casei 32G doses were utilized and hence the influence of dose on microbiota composition is the primary objective of this study. A secondary objective of this study was to evaluate the ability of 32G to reduce the clostridial population in GIT of mice. The piglet studies had demonstrated that the level of Clostridium was reduced in the ileum microbiotas upon 32G administration.

This observation had lead to the hypothesis that L. casei 32G may be able to prevent

Clostridium difficile infections. The development of a mouse model would allow for investigations into the mechanisms by which 32G alters the composition of the GIT microbiota.

Effect of L. casei 32G administration on Lactobacillus population. The result of enumeration of Lactobacillus in the cecum at different time points after the last dose is presented in Figure 1a. The mice receiving the low dose of L. casei 32G had similar numbers of lactobacilli as the controls at all time points (Table 1). The mice receiving the medium dose of L. casei 32G had significantly (p < 0.05) higher numbers of lactobacilli than 152 the controls at the 0, 6, and 12 h time points, while there was not a significant difference at the 3 and 24 h time points (Table 1). The mice receiving the high dose of L. casei 32G had significantly (p < 0.05) higher numbers of lactobacilli than the controls at all the time points with the exception of the 3 h time point, which L. casei 32G administration did not significantly affect the Lactobacillus numbers (Table 1). These results are in agreement with our previous studies demonstrating that the number of Lactobacillus in the GIT increased during the time that the bolus of probiotics passed through the GI compartment being examined. It is tempting to conclude that the increase in Lactobacillus numbers is directly related to the presence of the consumed 32G culture; however, it is possible that the observed increases in lactobacilli may be due to stimulation of commensal lactobacilli, rather than simply passage of 32G. Promotion of closely related bacteria after probiotic administration was previously reported to result from the consumption of L. rhamnosus GG, with increased levels of L. crispatus, L. salivarius, L.sakei, L. manihotivorans, L. suntoryeus, L. kitasatonis,

L. cypricasei, and L. fuchuensis being observed in human infants (2).

Effect of L. casei 32G administration on coliform bacteria. The results of enumeration of coliforms in the cecum at different time points after the last dose are presented in Figure 1b. The mice receiving the low dose of L. casei 32G had significantly (p

< 0.05) lower numbers of coliforms than the control at the 24 h time point, while the numbers were not significantly different for the other time points examined (Table 1). The mice receiving the medium dose of L. casei 32G had significantly (p < 0.05) lower numbers of coliforms than the control at the 24 h time point, while the numbers were not significantly different for the other time points examined (Table 1). The mice receiving the high dose of 153

L. casei 32G had significantly (p < 0.05) higher numbers of coliforms than the control at the

0 h time point, while the numbers were not significantly different for the other time points examined (Table 1). Similar analyses were not conducted in our piglet trials. However, significant increases were observed in Escherichia, a coliform, numbers based upon 16S rRNA pyrosequencing results in our second piglet trial (Chapter 4). A number of pathogens are coliforms and hence their reduction is commonly considered positive for health (10); however, coliforms are also a common component of the commensal GI microbiota and their impact on health is likely species and strain dependent.

Effect of L. casei 32G administration on class Clostridia population. The levels of class Clostridia were comparable to Lactobacillus numbers in the mouse cecum.

Administration of L. casei 32G did not significantly affect class Clostridia numbers (Table 1) regardless of the dose at any of the time points. The identity of the organisms enumerated on the Brucella agar plates were determined by 16S rRNA sequencing (Table 2) and the primarily genera identified were Paenibacillus and Ruminococcus. These genera were not detected as significant components of the microbiota of the piglet ileum microbiotas in either of our previous studies. These differences could be due to either differences in the host being examined (mice vs. pigs) or the GI compartment being analyzed (cecum vs. ileum).

Differences in methods to enumerate (plating on Brucella agar vs. 16S rRNA pyrosequencing) make it impossible to compare Clostridia preponderance between the current study and the previous piglet trials (Chapters 3 and 4).

Changes in microbiota composition after L. casei 32G administration. To assess the influence of administration of L. casei 32G on the microbiota of the mouse cecum, 154

ARISA was conducted. A total of 221 phylotypes ranging 307-1,051 bp were detected in the samples; each phylotype likely represents a bacterial species. Administration of the low dose significantly (p < 0.05) increased the number of phylotypes detected, when compared to control group (27.4 ± 2.7 vs. 14.7 ± 2.3). Increases were also observed in the medium (19.9

± 2.1) and high (22.1 ± 1.8) dose groups; however, these values did not differ significantly (p

> 0.05) from those present in the control (14.7 ± 2.3). L. casei (phylotypes 502 and 709) was not detected in cecum samples from either the control or 32G low dose mice, while they comprised 0.07 ± 0.05 and 6.90 ± 2.31% of the amplicons present in the medium and high dose mice, respectively. Higher diversity of intestinal lactobacilli was previously reported in mice after receiving L. casei and L. plantarum, although overall intestinal microbiota was not affected (8).

Between-group analysis was utilized to determine if the overall microbiota composition differed between the 32G-fed and the control mice (Figure 2). Administration of the low 32G dose resulted in a significant change in mouse cecum microbiota in comparison to the control, medium dose, and high dose microbiotas (Figure 2a); the microbiota present in the cecum of mice receiving the low dose, with the exception of the 12 h time point, did not differ significantly from each other (Figure 2b). Administration of the medium and high 32G doses resulted in microbiota that were significantly different than the control and low dose microbiota at most time points (Figure 2b) but, in general, did not differ from each other. These results strongly suggest that alterations in the mouse cecum microbiota composition by L. casei 32G are dose dependent, with the lowest dose examined

(106 CFU/day for seven days) having the greatest impact. The medium (107 CFU/day for 155 seven days) and high (108 CFU/day for seven days) doses had similar impacts on the mouse cecum microbiota composition; the observed changes were different and less dramatic relative to the control, than those observed with the low dose of 32G. These results support the conclusions of the two previous piglet trials in that 32G consumption can result in significant changes to the GI microbiota and that these changes are not simply due to dilution of the commensal microbiota by the addition of the 32G. 156

Conclusion

This study demonstrated that dose dependent effects occur in the cecum of mice upon administration of L. casei 32G. These effects include increases in numbers of lactobacilli detected by enumeration on Rogosa SL and numbers of L. casei detected by

ARISA, and changes in the composition of the cecum microbiota detected by ARISA. The availability of a mouse model will allow for the mechanism(s) by which 32G affects these changes to be examined. Further research is required to determine if these changes in the

GIT microbiota have an influence on the health of the host. However, these results clearly demonstrate that probiotic dose can have a significant impact on the GIT microbiota and that, at least some cases, a lower dose can have a more dramatic impact on the microbiota. This study sets the stage for future studies on the mechanism(s) by which L. casei 32G influences the composition of the GIT microbiota and the potential health effects of these changes. 157

Acknowledgements

We appreciate the technical contributions from Dr. Ekkarat Phrommao, Jeehwan Oh,

Elena Vinay-Lara, Jessie Heidenreich, Neil Gandhi, Davide Porcellato, Chokchai Chuea- nongthon, Pingfan Wu, and Michael Donath. Peggy Steele, a member of Steele’s family, is an employee of DuPont Inc. 158

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8. Fuentes, S., M. Egert, M. Jimenez-Valera, A. Ramos-Cormenzana, A. Ruiz-Bravo, H. Smidt, and M. Monteoliva-Sanchez. 2008. Administration of Lactobacillus casei and Lactobacillus plantarum affects the diversity of murine intestinal lactobacilli, but not the overall bacterial community structure. Res Microbiol 159:237-243.

9. Gao, X. W., M. Mubasher, C. Y. Fang, C. Reifer, and L. E. Miller. 2010. Dose- response efficacy of a proprietary probiotic formula of Lactobacillus acidophilus CL1285 and Lactobacillus casei LBC80R for antibiotic-associated diarrhea and Clostridium difficile-associated diarrhea prophylaxis in adult patients. Am J Gastroenterol 105:1636- 1641. 159

10. Gill, H., and J. Prasad. 2008. Probiotics, immunomodulation, and health benefits. Adv Exp Med Biol 606:423-454.

11. Hojo, K., S. Nagaoka, S. Murata, N. Taketomo, T. Ohshima, and N. Maeda. 2007. Reduction of vitamin K concentration by salivary Bifidobacterium strains and their possible nutritional competition with Porphyromonas gingivalis. J. Appl. Microbiol. 103:1969-1974.

12. Jones, S. E., A. L. Shade, K. D. McMahon, and A. D. Kent. 2007. Comparison of primer sets for use in automated ribosomal intergenic spacer analysis of aquatic bacterial communities: an ecological perspective. Appl. Environ. Microbiol. 73:659-662.

13. Li, X. Q., Y. H. Zhu, H. F. Zhang, Y. Yue, Z. X. Cai, Q. P. Lu, L. Zhang, X. G. Weng, F. J. Zhang, D. Zhou, J. C. Yang, and J. F. Wang. 2012. Risks associated with high-dose Lactobacillus rhamnosus in an Escherichia coli model of piglet diarrhoea: intestinal microbiota and immune imbalances. PLoS One 7:e40666.

14. Liu, Z., T. Shen, P. Zhang, Y. Ma, and H. Qin. 2011. Lactobacillus plantarum surface layer adhesive protein protects intestinal epithelial cells against tight junction injury induced by enteropathogenic Escherichia coli. Mol Biol Rep 38:3471-3480.

15. Miles, A. A., S. S. Misra, and J. O. Irwin. 1938. The estimation of the bactericidal power of the blood. J Hyg (Lond) 38:732-749.

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19. Yang, Z. Q., C. J. Jin, L. Gao, W. M. Fang, R. X. Gu, J. Y. Qian, and X. A. Jiao. 2012. Alleviating effects of Lactobacillus strains on pathogenic Vibrio parahaemolyticus- induced intestinal fluid accumulation in the mouse model. FEMS Microbiol Lett:doi: 10.1111/1574-6968.12050.

160

(a) (b)

(c)

Figure 1. Detection of (a) Lactobacillus, (b) coliform bacteria, and (c) class Clostridia in the cecum of control mice (—◊—), as well as mice fed either low (····□····), medium (---Δ---), or high (– –○– –) dose of L. casei 32G. 161

(a) (b)

Figure 2. Composition of mouse cecum microbiota characterized by ARISA. The diagram shows the results of between-group analysis applied to correspondence analysis using (a) doses (control, low, medium, and high) or (b) treatments (combinations of doses and sampling time) as the constraint on the microbiota data. Ellipses are centered on the biological replicates, which are shown as dots, and represent the collective variance for each dose or treatment. Lines connect the doses or treatments to their biological replicates. The medium and high dose ellipses are in black while the control and low dose ellipses are in blue and red, respectively. 162

Table 1. Average numbers of Lactobacillus, coliform bacteria, and class Clostridia (log CFU/ml) in the cecum of control mice and mice receiving low, medium, or high doses of L. casei 32G. Bacteria Time (h) Control Low Medium High 0 5.58  0.18B 5.13  0.35B 7.35  0.24A 5.79  0.10B 3 6.19  0.03A 5.54  0.97A 5.51  0.22A 6.88  0.18A 6 5.59  0.29B 4.69  0.05B 6.79  0.28A 6.90  0.12A Lactobacillus 12 4.95  0.13B 5.32  0.28AB 5.84  0.14A 5.90  0.29A 24 5.56  0.32B 5.74  0.44B 6.02  0.48AB 7.35  0.09A Average 5.57  0.11B 5.32  0.24B 6.31  0.16A 6.58  0.12A 0 3.72  0.26BC 2.91  0.15C 5.09  0.41AB 5.39  0.42A 3 3.93  0.23A 3.30  0.32A 3.41  0.45A 4.33  0.50A 6 4.31  0.09A 3.48  0.10A 2.59  0.83A 2.92  0.92A Coliform 12 2.87  0.91A 2.88  0.93A 2.19  0.81A 3.89  0.07A 24 4.29  0.06A 0.41  0.41C 3.10  0.33B 4.61  0.09A Average 3.82  0.21A 2.59  0.29B 3.39  0.31AB 4.27  0.26A 0 3.33  3.33A 6.45  0.19A 5.67  0.33A 5.89  0.37A 3 6.51  0.32AB 7.14  0.20A 5.20  0.31B 5.32  0.33B Class 6 6.60  0.41A 4.01  2.01A 6.88  0.52A 7.18  0.27A Clostridia 12 3.73  1.88A 5.82  0.28A 6.03  0.36A 6.36  0.17A 24 6.56  0.33A 6.68  0.30A 6.32  0.22A 6.07  0.18A Average 5.35  0.77A 6.02  0.46A 6.02  0.20A 6.16  0.19A Note: the detection limit is 100 CFU/ml. Values in the same row with different letters differ statistically (p < 0.05). 163

Table 2. Identification of bacterial colonies isolated from Brucella agar plates by comparison of partial 16S rRNA sequences to the Ribosomal Database Project (RDP). Group Size (bp) Species S_ab score Control 979 Paenibacillus barengoltzii 0.979 Control 984 Paenibacillus campinasensis 0.982 low dose 955 Ruminococcus gnavus 0.759 Medium dose 959 Blautia coccoides 0.727 Medium dose 958 Ruminococcus gnavus 0.814 Medium dose 972 Clostridium scindens 0.835 High dose 957 Hespellia porcina 0.781 High dose 971 Desulfotomaculum guttoideum 0.946

164

CHAPTER 6

Summary and recommendation for future studies

165

Summary

The intent of my Ph.D. research was to initiate a research in the Steele laboratory in the area of L. casei as a probiotic. The Steele laboratory has established a large collection (> 60 strains) of L. casei strains from diverse ecological niches including cheese, plant materials (e.g., wine, pickles, and corn silage) and humans (blood and feces). Characterization of genetic diversity within this collection has been a major focus in the Steele laboratory over the past eight years. Multilocus sequence typing has revealed the divergence of three major lineages of L. casei approximately 1.5 million years ago (2). The genomic sequence of the neotype strain, L. casei ATCC 334, has been determined and a detailed analysis of the genome has been published

(3). Finally, comparative analysis of 17 L. casei genomes has revealed an average genome content of 2,780 genes and that the species has a predicted core genome of 1,715 genes, indicating that approximately 38% of the genes are variable in an average L. casei genome (1).

These results demonstrate that significant strain-to-strain variation exists within this species and that significant strain-to-strain differences in probiotic efficacy and effects are likely. To achieve my goal of initiating a research program in the Steele laboratory on L. casei as a probiotic, I have established methods for screening L. casei strains for attributes commonly associated with probiotics through the development of in vitro and in vivo models, and utilized culture- independent methods to characterize the influence of consumption of L. casei on the composition of the gastrointestinal tract (GIT) microbiota of piglets and mice.

In Chapter 3, in vitro and in vivo (piglets consuming a humanized diet) models were developed to screen of L. casei strains for differences in the ability to survive GIT passage to the ileum. This section of the GIT was selected based upon its importance in modulating 166 mammalian immune function (4). The two models yielded different survival rates, but similar rank orders of survival, with L. casei DN-114001 (the most common L. casei strain sold world- wide as a probiotic) exhibiting the least survival, indicating that the in vitro model has utility in screening potential probiotic strains. Adherence of the four strains to piglet ileum was examined and L. casei 32G was determined to adhere at a significantly higher level than the other three strains examined. These results demonstrate that strains of L. casei differ in attributes commonly associated with probiotic efficacy and hence support the hypothesis that significant strain-to- strain differences in probiotic efficacy are present within L. casei. The ability of L. casei 32G to survive passage to the ileum and adhere to the piglet ileum epithelial surface resulted in this strain being selected for further characterization. To assess the ability of 32G to alter the ileum digesta and tissue adherent microbiotas, 16S rRNA pyrosequencing was conducted. Significant changes were detected in the dominant genera in both the digesta and tissue samples. The numerically most significant differences between control and 32G-fed piglet ileum digesta microbiotas were reductions in the preponderance of Turicibacter (30.05 vs. 0.08%) and

Clostridium (22.96 vs. 0.08%) and increases in the percentage of Lactobacillus (15.86 vs.

38.28%) and Actinobacillus (< 0.0005 vs. 15.83%). These results indicate that L. casei 32G has survival, adherence, and antimicrobial characteristics desirable in a probiotic strain and that feeding of L. casei 32G results in a microbe-specific restructuring of the commensal ileum microbiotas. This observation has both broad theoretical implications and 32G-specific practical implications. The theoretical implications relate to the selection of immunomodulatory probiotic strains, which typically is based upon their ability to directly influence the expression of various immune related genes (e.g. cytokines). However, our results suggest that immunomodulatory probiotics could also act indirectly, wherein consumption of a probiotic results in restructuring of 167 the ileum microbiota, whose components are in turn responsible for modulating the immune system. For example, if a probiotic altered the ileum microbiota toward higher levels of microorganisms with lipopolysaccharide, the net result could be increased inflammation, although probiotic itself was not directly pro-inflammatory. However, this study only examined the ability of 32G to influence the composition of these microbiotas at a single time point and in piglets with a specific genetic makeup; therefore, additional studies were required to determine how broadly these results could be applied.

The research presented in Chapter 4 repeated the piglet feeding trial presented in Chapter

3, with the following modifications: 1) a lower dose of 32G was administrated (from 1010 to 109

CFU/day); 2) the piglets had a different genetic make-up; 3) the piglets were housed under different conditions; and 4) changes in the ileum digesta and tissue (Peyer’s patch in this study) adherent microbiotas were followed for 72 h after administration of the last 32G dose. The ileum digesta and Peyer’s patch adherent microbiotas were determined by 16S rRNA pyrosequencing and the composition of the control microbiotas were determined to differ significantly between the two studies. Possible reasons for these differences include differences in piglet genetics, housing environments, and the samples analyzed in the case of the adherent microbiotas (Peyer’s patch vs. ileum cross-sections). Analysis of the these microbiotas as a function of time indicates that daily consumption of 32G resulted in significant, relatively short- lived alterations to the composition of both the digesta and Peyer’s patch microbiotas, with the digesta microbiota returning to compositions that were not distinguishable from the control microbiotas within 24 h. Of particular interest in this trial was the increases in numbers of

Escherichia and Lactobacillus that occurred at the 6 and 12 h time points after the consumption of L. casei 32G; these increases occurred in both the digesta and Peyer’s patch samples. These 168 results suggest that 32G indirect immunomodulation, in this case by increasing numbers of

Escherichia in the ileum, may be an important immunomodulatory mechanism. Additionally, a significant decrease was observed in the preponderance of Clostridium in the ileum digesta microbiota, as was observed in the previous piglet trial (Chapter 3); this result supports the hypothesis that 32G may have potential for the control of Clostridium difficile infection, a serious medical condition that has been shown to be prevented by administration of a probiotic preparation containing L. casei (5). However, the only definitive conclusion that can be drawn from these results is that 32G consumption results in an alteration of the composition of the ileum microbiota; the potential health implications of these alterations remain unknown.

In Chapter 5, we investigated the effects of L. casei 32G dose on this strain’s ability to alter the composition of the cecum microbiota of mice. The development of a mouse model was undertaken due to advantages inherent to utilizing mice as experimental models. These advantages include significant lower costs associated with procuring and housing mice relative to piglets, as well as the availability of a variety of genetically engineered mice lacking specific genes related to immune function (e.g. lacking IL-10). In this study, different doses of L. casei

32G were administered to mice for seven days and the numbers of Lactobacillus, coliform bacteria, and class Clostridia were enumerated at 0, 3, 6, 12, and 24 h after the last dose.

Additionally, the composition of cecum microbiota was characterized using automated ribosomal intergenic spacer analysis (ARISA). The results indicate that 32G administration at the medium and high doses resulted in an increase in numbers of lactobacilli present in the cecum digesta. In general, none of the doses examined influenced the numbers of clostridia and coliforms.

Administration of the low 32G dose resulted in significant changes in the mouse cecum microbiota, in comparison to the control, medium dose, and high dose microbiotas. 169

Administration of the medium and high 32G doses resulted in microbiotas that were significantly different then the control and low dose microbiotas at most time points but, in general, did not differ from each other. This study demonstrated that dose-dependent changes occur in the cecum microbiota of mice upon administration of L. casei 32G. These results clearly demonstrate that probiotic dose can have a significant impact on the GIT microbiota and that, at least some cases, a lower dose can have a more dramatic impact on this microbiota. This study sets the stage for future studies on the mechanism(s) by which L. casei 32G influences the composition of the GIT microbiota and the potential health effects of these changes.

The results presented in thesis provide a base for future research in the Steele laboratory on the use of L. casei strains as probiotics for a variety of human diseases. Models for in vitro and in vivo (piglets being fed a humanized diet and mice on a standard diet) evaluation of probiotic attributes have been developed. Additionally, screening of four L. casei strains resulted in the selection of 32G for additional studies as a probiotic. L. casei 32G has been shown to cause restructuring of the GIT microbiotas in both the piglet and mouse models using 16S rRNA-based methods and preliminary results suggest that this strain may have utility in reducing

C. difficile infection. 170

Recommendations for future studies

There are a large number of different directions that future research in this area could proceed. The suggestions below are simply my view of possible next steps.

1) The mouse model should be refined through the use of 16S rRNA pyrosequencing to

characterized the specific changes at the genus level that occur in the mouse cecum

microbiota upon administration of L. casei strains as a function of dose. These results

are essential to the development of mouse models for evaluating the mechanisms by

which L. casei alters the GIT microbiota and the potential health effects of these

changes.

2) L. casei strains should be screened for the ability to prevent or treat the infectious

diseases such as Clostridium difficile infection in mice. This model will allow for the

selection of specific strains for defined health outcomes and provide tools for

evaluating their mechanisms of action.

3) The safety of potential probiotic strains of L. casei should be evaluated by in silico

analysis for genes involved in virulence, antibiotic resistance, and the production of

harmful metabolites. 171

References

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2. Cai, H., B. T. Rodriguez, W. Zhang, J. R. Broadbent, and J. L. Steele. 2007. Genotypic and phenotypic characterization of Lactobacillus casei strains isolated from different ecological niches suggests frequent recombination and niche specificity. Microbiology 153:2655-2665.

3. Cai, H., R. Thompson, M. F. Budinich, J. R. Broadbent, and J. L. Steele. 2009. Genome sequence and comparative genome analysis of Lactobacillus paracasei: insights into their niche-associated evolution. Genome Biol Evol 1:239-257.

4. Plant, L., and P. Conway. 2001. Association of Lactobacillus spp. with Peyer's patches in mice. Clin Diagn Lab Immunol 8:320-324.

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